2022
Lekssays, Ahmed; Sirigu, Giorgia; Carminati, Barbara; Ferrari, Elena
MalRec: A Blockchain-based Malware Recovery Framework for Internet of Things Proceedings Article
In: ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security, 2022.
@inproceedings{lekssays2022malrec,
title = {MalRec: A Blockchain-based Malware Recovery Framework for Internet of Things},
author = {Ahmed Lekssays and Giorgia Sirigu and Barbara Carminati and Elena Ferrari},
url = {https://dl.acm.org/doi/abs/10.1145/3538969.3544446},
doi = {10.1145/3538969.3544446},
year = {2022},
date = {2022-08-23},
booktitle = {ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security},
abstract = {IoT devices have been considered an attractive target for malware (e.g., botnets) due to their low computational resources and lack of security measures. The literature focuses on detecting malware, but less attention is given to recovery solutions. In addition, with the development of data processing regulations in different countries, a need for transparent recovery systems that can help organizations present their due diligence arises. This work proposes a blockchain-based backup policy enforcement framework for IoT where an organization can formalize backup policies and enforce them. We have run our solution under extensive tests that show that it can be deployed in real-life IoT environments, despite the limited computational resources of IoT devices.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
IoT devices have been considered an attractive target for malware (e.g., botnets) due to their low computational resources and lack of security measures. The literature focuses on detecting malware, but less attention is given to recovery solutions. In addition, with the development of data processing regulations in different countries, a need for transparent recovery systems that can help organizations present their due diligence arises. This work proposes a blockchain-based backup policy enforcement framework for IoT where an organization can formalize backup policies and enforce them. We have run our solution under extensive tests that show that it can be deployed in real-life IoT environments, despite the limited computational resources of IoT devices.
Son, Ha Xuan; Carminati, Barbara; Ferrari, Elena
A Risk Estimation Mechanism for Android Apps based on Hybrid Analysis Journal Article
In: Data Science and Engineering, 2022.
@article{son2022risk,
title = {A Risk Estimation Mechanism for Android Apps based on Hybrid Analysis},
author = {Ha Xuan Son and Barbara Carminati and Elena Ferrari },
url = {https://link.springer.com/article/10.1007/s41019-022-00189-1},
doi = {10.1007/s41019-022-00189-1},
year = {2022},
date = {2022-07-29},
urldate = {2022-07-29},
journal = {Data Science and Engineering},
abstract = {Mobile apps represent essential tools in our daily routines, supporting us in almost every task. However, this assistance might imply a high cost in terms of privacy. Indeed, mobile apps gather a massive amount of data about individuals (e.g., users’ profiles and habits) and their devices (e.g., locations), where not all are strictly needed for app execution. According to privacy laws, apps’ providers must inform end-users on adopted data usage practices (e.g., which data are collected and for which purpose). Unfortunately, understanding these practices is a complex task for average end-users. The result is that they install apps without understanding their privacy implications. To support users in making more privacy-aware decisions on app usage, we propose a risk estimation approach based on an analysis of the app’s code. This analysis adopts a hybrid strategy, exploiting static and dynamic code analyses. Static analysis aims at discovering which personal data an app is collecting to determine whether the target app is asking more than required. This gives the first estimation of the app’s risk level. In addition, we also perform a dynamic analysis of the target app’s code. This further analysis helps determining whether the collected personal data is consumed locally on the mobile device or sent out to external services. If this happens, the risk level has to be increased, as personal data are more exposed. To prove the proposal’s effectiveness, we run several experiments involving different groups of participants. The obtained accuracy results are promising and outperform those obtained with static analysis only.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mobile apps represent essential tools in our daily routines, supporting us in almost every task. However, this assistance might imply a high cost in terms of privacy. Indeed, mobile apps gather a massive amount of data about individuals (e.g., users’ profiles and habits) and their devices (e.g., locations), where not all are strictly needed for app execution. According to privacy laws, apps’ providers must inform end-users on adopted data usage practices (e.g., which data are collected and for which purpose). Unfortunately, understanding these practices is a complex task for average end-users. The result is that they install apps without understanding their privacy implications. To support users in making more privacy-aware decisions on app usage, we propose a risk estimation approach based on an analysis of the app’s code. This analysis adopts a hybrid strategy, exploiting static and dynamic code analyses. Static analysis aims at discovering which personal data an app is collecting to determine whether the target app is asking more than required. This gives the first estimation of the app’s risk level. In addition, we also perform a dynamic analysis of the target app’s code. This further analysis helps determining whether the collected personal data is consumed locally on the mobile device or sent out to external services. If this happens, the risk level has to be increased, as personal data are more exposed. To prove the proposal’s effectiveness, we run several experiments involving different groups of participants. The obtained accuracy results are promising and outperform those obtained with static analysis only.
2021
Lekssays, Ahmed; Landa, Luca; Carminati, Barbara; Ferrari, Elena
PAutoBotCatcher: A blockchain-based privacy-preserving botnet detector for Internet of Things Journal Article
In: Computer Networks, pp. 108512, 2021, ISSN: 1389-1286.
@article{LEKSSAYS2021108512,
title = {PAutoBotCatcher: A blockchain-based privacy-preserving botnet detector for Internet of Things},
author = {Ahmed Lekssays and Luca Landa and Barbara Carminati and Elena Ferrari},
url = {https://www.sciencedirect.com/science/article/pii/S138912862100445X},
doi = {https://doi.org/10.1016/j.comnet.2021.108512},
issn = {1389-1286},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Computer Networks},
pages = {108512},
abstract = {Botnets have become a major threat in the Internet of Things (IoT) landscape, due to the damages that these sets of compromised IoT devices may cause. To increase their attacks’ success, modern botnets are designed in a distributed manner, following a P2P structure. Recently, several botnet detection solutions have been proposed. Among them, community behavior analysis solutions seem to be promising because of their high detection accuracy. However, such solutions are not optimized for real life scenarios since they only run in a static mode, that is, reading all network traffic at once. As such, they do not support real-time data analysis. In order to handle such issue, these solutions should run in a dynamic distributed environment where different actors participate in the detection process. However, this collaborative environment brings up the issue of trust among the actors. To address this issue, in this paper, we present PAutoBotCatcher, a dynamic botnet detection framework based on community behavior analysis among peers managed by different actors. PAutoBotCatcher leverages on blockchain to ensure immutability and transparency among all actors. To optimize continuous detection while keeping good accuracy, we design a set of optimization techniques, such as caching detection’s output and pre-processing the shared network traffic. In addition, we leverage on different privacy-preserving techniques to protect devices from re-identification during the botnet detection process. We have extensively tested our solution to show its effectiveness and to demonstrate that blockchain is a good solution for dynamic botnet detection.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Botnets have become a major threat in the Internet of Things (IoT) landscape, due to the damages that these sets of compromised IoT devices may cause. To increase their attacks’ success, modern botnets are designed in a distributed manner, following a P2P structure. Recently, several botnet detection solutions have been proposed. Among them, community behavior analysis solutions seem to be promising because of their high detection accuracy. However, such solutions are not optimized for real life scenarios since they only run in a static mode, that is, reading all network traffic at once. As such, they do not support real-time data analysis. In order to handle such issue, these solutions should run in a dynamic distributed environment where different actors participate in the detection process. However, this collaborative environment brings up the issue of trust among the actors. To address this issue, in this paper, we present PAutoBotCatcher, a dynamic botnet detection framework based on community behavior analysis among peers managed by different actors. PAutoBotCatcher leverages on blockchain to ensure immutability and transparency among all actors. To optimize continuous detection while keeping good accuracy, we design a set of optimization techniques, such as caching detection’s output and pre-processing the shared network traffic. In addition, we leverage on different privacy-preserving techniques to protect devices from re-identification during the botnet detection process. We have extensively tested our solution to show its effectiveness and to demonstrate that blockchain is a good solution for dynamic botnet detection.
Daidone, Federico; Carminati, Barbara; Ferrari, Elena
Blockchain-based Privacy Enforcement in the IoT domain Journal Article
In: IEEE Transactions on Dependable and Secure Computing, 2021.
@article{daidone2021blockchain,
title = {Blockchain-based Privacy Enforcement in the IoT domain},
author = {Federico Daidone and Barbara Carminati and Elena Ferrari},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Dependable and Secure Computing},
publisher = {IEEE},
abstract = {The Internet of Things (IoT) pervades our lives every day and has given end users the opportunity of accessing personalized and advanced services based on the analysis of the sensed data. However, IoT services are also characterized by new challenges related to security and privacy because end users often share sensitive data with different consumers without precise knowledge of how they will be managed and used. To cope with these issues, we propose a blockchain-based privacy enforcement framework where users can define how their data can be used and check if their will is respected without relying on a centralized manager. The preliminary tests we performed, simulating different scenarios, show the feasibility of our approach.},
keywords = {},
pubstate = {published},
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}
The Internet of Things (IoT) pervades our lives every day and has given end users the opportunity of accessing personalized and advanced services based on the analysis of the sensed data. However, IoT services are also characterized by new challenges related to security and privacy because end users often share sensitive data with different consumers without precise knowledge of how they will be managed and used. To cope with these issues, we propose a blockchain-based privacy enforcement framework where users can define how their data can be used and check if their will is respected without relying on a centralized manager. The preliminary tests we performed, simulating different scenarios, show the feasibility of our approach.
Giaretta, Lodovico; Lekssays, Ahmed; Carminati, Barbara; Ferrari, Elena; Girdzijauskas, Sarunas
LiMNet: Early-Stage Detection of IoT Botnets with Lightweight Memory Networks Proceedings Article
In: Bertino, Elisa; Shulman, Haya; Waidner, Michael (Ed.): Computer Security - ESORICS 2021 - 26th European Symposium on Research in Computer Security, Darmstadt, Germany, October 4-8, 2021, Proceedings, Part I, pp. 605–625, Springer, 2021.
@inproceedings{DBLP:conf/esorics/GiarettaLCFG21,
title = {LiMNet: Early-Stage Detection of IoT Botnets with Lightweight Memory Networks},
author = {Lodovico Giaretta and Ahmed Lekssays and Barbara Carminati and Elena Ferrari and Sarunas Girdzijauskas},
editor = {Elisa Bertino and Haya Shulman and Michael Waidner},
url = {https://doi.org/10.1007/978-3-030-88418-5_29},
doi = {10.1007/978-3-030-88418-5_29},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Computer Security - ESORICS 2021 - 26th European Symposium on Research
in Computer Security, Darmstadt, Germany, October 4-8, 2021, Proceedings,
Part I},
volume = {12972},
pages = {605--625},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
abstract = {IoT devices have been growing exponentially in the last few years. This growth makes them an attractive target for attackers due to their low computational power and limited security features. Attackers use IoT botnets as an instrument to perform DDoS attacks which caused major disruptions of Internet services in the last decade. While many works have tackled the task of detecting botnet attacks, only a few have considered early-stage detection of these botnets during their propagation phase.While previous approaches analyze each network packet individually to predict its maliciousness, we propose a novel deep learning model called LiMNet (Lightweight Memory Network), which uses an internal memory component to capture the behaviour of each IoT device over time. This memory incorporates both packet features and behaviour of the peer devices. With this information, LiMNet achieves almost maximum AUROC classification scores, between 98.8% and 99.7%, with a 14% improvement over state of the art. LiMNet is also lightweight, performing inference almost 8 times faster than previous approaches.},
keywords = {},
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tppubtype = {inproceedings}
}
IoT devices have been growing exponentially in the last few years. This growth makes them an attractive target for attackers due to their low computational power and limited security features. Attackers use IoT botnets as an instrument to perform DDoS attacks which caused major disruptions of Internet services in the last decade. While many works have tackled the task of detecting botnet attacks, only a few have considered early-stage detection of these botnets during their propagation phase.While previous approaches analyze each network packet individually to predict its maliciousness, we propose a novel deep learning model called LiMNet (Lightweight Memory Network), which uses an internal memory component to capture the behaviour of each IoT device over time. This memory incorporates both packet features and behaviour of the peer devices. With this information, LiMNet achieves almost maximum AUROC classification scores, between 98.8% and 99.7%, with a 14% improvement over state of the art. LiMNet is also lightweight, performing inference almost 8 times faster than previous approaches.
Colombo, Pietro; Ferrari, Elena
Evaluating the effects of access control policies within NoSQL systems Journal Article
In: Future Gener. Comput. Syst., vol. 114, pp. 491–505, 2021.
@article{DBLP:journals/fgcs/ColomboF21,
title = {Evaluating the effects of access control policies within NoSQL systems},
author = {Pietro Colombo and Elena Ferrari},
url = {https://doi.org/10.1016/j.future.2020.08.026},
doi = {10.1016/j.future.2020.08.026},
year = {2021},
date = {2021-01-01},
journal = {Future Gener. Comput. Syst.},
volume = {114},
pages = {491--505},
abstract = {Access control is a key service of any data management system. It allows regulating the access to data resources at different granularity levels on the basis of access control models which vary on the protection options they offer. The more powerful is the access control model in terms of protection requirements, the more difficult is for security administrators to understand the effect of a set of access control policies on the protected resources. This is further complicated within schemaless systems, like NoSQL datastores, when fine grained access control policies are specified for data resources characterized by heterogeneous structures. The lack of a reference data model and related manipulation languages exacerbates this issue. To the best of our knowledge, a general approach to evaluate the impact of access control policies on the protected resources within NoSQL systems is still missing. In this paper, we start to fill this void, by proposing a data model agnostic approach, which, starting from schemaless datasets protected by different discretionary access control models, derives a view of the protected resources that points out authorized and unauthorized contents. Experimental results show the approach efficiency even with large datasets.},
keywords = {},
pubstate = {published},
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Access control is a key service of any data management system. It allows regulating the access to data resources at different granularity levels on the basis of access control models which vary on the protection options they offer. The more powerful is the access control model in terms of protection requirements, the more difficult is for security administrators to understand the effect of a set of access control policies on the protected resources. This is further complicated within schemaless systems, like NoSQL datastores, when fine grained access control policies are specified for data resources characterized by heterogeneous structures. The lack of a reference data model and related manipulation languages exacerbates this issue. To the best of our knowledge, a general approach to evaluate the impact of access control policies on the protected resources within NoSQL systems is still missing. In this paper, we start to fill this void, by proposing a data model agnostic approach, which, starting from schemaless datasets protected by different discretionary access control models, derives a view of the protected resources that points out authorized and unauthorized contents. Experimental results show the approach efficiency even with large datasets.
Colombo, Pietro; Ferrari, Elena; Tümer, Engin Deniz
Regulating data sharing across MQTT environments Journal Article
In: J. Netw. Comput. Appl., vol. 174, pp. 102907, 2021.
@article{DBLP:journals/jnca/ColomboFT21,
title = {Regulating data sharing across MQTT environments},
author = {Pietro Colombo and Elena Ferrari and Engin Deniz T\"{u}mer},
url = {https://doi.org/10.1016/j.jnca.2020.102907},
doi = {10.1016/j.jnca.2020.102907},
year = {2021},
date = {2021-01-01},
journal = {J. Netw. Comput. Appl.},
volume = {174},
pages = {102907},
abstract = {Nowadays, due to the personal nature of the managed data, numerous Internet of Things (IoT) applications represent a potential threat to user privacy. In order to address this issue, several access control models have been specifically designed for IoT. The great majority of these proposals adopt centralized enforcement mechanisms designed to control the communication of IoT devices operating in the same environment. However, these approaches cannot regulate data exchange operated by devices connected to different environments. To the best of our knowledge, effective approaches capable of controlling these forms of communications are still missing. Therefore, in this paper, we do a step to fill this void, by focusing on applications built on top of MQTT, a widely used protocol for IoT. We propose an access control framework to regulate data sharing across bridged MQTT environments, on the basis of both access control policies and user preferences. The proposed approach regulates data exchange among IoT devices belonging to interconnected environments by means of a decentralized enforcement mechanism. Experimental analyses show the efficiency of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nowadays, due to the personal nature of the managed data, numerous Internet of Things (IoT) applications represent a potential threat to user privacy. In order to address this issue, several access control models have been specifically designed for IoT. The great majority of these proposals adopt centralized enforcement mechanisms designed to control the communication of IoT devices operating in the same environment. However, these approaches cannot regulate data exchange operated by devices connected to different environments. To the best of our knowledge, effective approaches capable of controlling these forms of communications are still missing. Therefore, in this paper, we do a step to fill this void, by focusing on applications built on top of MQTT, a widely used protocol for IoT. We propose an access control framework to regulate data sharing across bridged MQTT environments, on the basis of both access control policies and user preferences. The proposed approach regulates data exchange among IoT devices belonging to interconnected environments by means of a decentralized enforcement mechanism. Experimental analyses show the efficiency of the proposed approach.
Singh, Bikash Chandra; Carminati, Barbara; Ferrari, Elena
Privacy-Aware Personal Data Storage (P-PDS): Learning how to Protect User Privacy from External Applications Journal Article
In: IEEE Trans. Dependable Secur. Comput., vol. 18, no. 2, pp. 889–903, 2021.
@article{DBLP:journals/tdsc/SinghCF21,
title = {Privacy-Aware Personal Data Storage (P-PDS): Learning how to Protect
User Privacy from External Applications},
author = {Bikash Chandra Singh and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1109/TDSC.2019.2903802},
doi = {10.1109/TDSC.2019.2903802},
year = {2021},
date = {2021-01-01},
journal = {IEEE Trans. Dependable Secur. Comput.},
volume = {18},
number = {2},
pages = {889--903},
abstract = {Recently, Personal Data Storage (PDS) has inaugurated a substantial change to the way people can store and control their personal data, by moving from a service-centric to a user-centric model. PDS offers individuals the capability to keep their data in a unique logical repository, that can be connected and exploited by proper analytical tools, or shared with third parties under the control of end users. Up to now, most of the research on PDS has focused on how to enforce user privacy preferences and how to secure data when stored into the PDS. In contrast, in this paper we aim at designing a Privacy-aware Personal Data Storage (P-PDS), that is, a PDS able to automatically take privacy-aware decisions on third parties access requests in accordance with user preferences. The proposed P-PDS is based on preliminary results presented in [1] , where it has been demonstrated that semi-supervised learning can be successfully exploited to make a PDS able to automatically decide whether an access request has to be authorized or not. In this paper, we have deeply revised the learning process in order to have a more usable P-PDS, in terms of reduced effort for the training phase, as well as a more conservative approach w.r.t. users privacy, when handling conflicting access requests. We run several experiments on a realistic dataset exploiting a group of 360 evaluators. The obtained results show the effectiveness of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recently, Personal Data Storage (PDS) has inaugurated a substantial change to the way people can store and control their personal data, by moving from a service-centric to a user-centric model. PDS offers individuals the capability to keep their data in a unique logical repository, that can be connected and exploited by proper analytical tools, or shared with third parties under the control of end users. Up to now, most of the research on PDS has focused on how to enforce user privacy preferences and how to secure data when stored into the PDS. In contrast, in this paper we aim at designing a Privacy-aware Personal Data Storage (P-PDS), that is, a PDS able to automatically take privacy-aware decisions on third parties access requests in accordance with user preferences. The proposed P-PDS is based on preliminary results presented in [1] , where it has been demonstrated that semi-supervised learning can be successfully exploited to make a PDS able to automatically decide whether an access request has to be authorized or not. In this paper, we have deeply revised the learning process in order to have a more usable P-PDS, in terms of reduced effort for the training phase, as well as a more conservative approach w.r.t. users privacy, when handling conflicting access requests. We run several experiments on a realistic dataset exploiting a group of 360 evaluators. The obtained results show the effectiveness of the proposed approach.
Hoang, Anh-Tu; Carminati, Barbara; Ferrari, Elena
Privacy-Preserving Sequential Publishing of Knowledge Graphs Proceedings Article
In: 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021, pp. 2021–2026, IEEE, 2021.
@inproceedings{DBLP:conf/icde/HoangCF21,
title = {Privacy-Preserving Sequential Publishing of Knowledge Graphs},
author = {Anh-Tu Hoang and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1109/ICDE51399.2021.00194},
doi = {10.1109/ICDE51399.2021.00194},
year = {2021},
date = {2021-01-01},
booktitle = {37th IEEE International Conference on Data Engineering, ICDE 2021,
Chania, Greece, April 19-22, 2021},
pages = {2021--2026},
publisher = {IEEE},
abstract = {We propose a graph-based framework for privacy preserving data publication, which is a systematic abstraction of existing anonymity approaches and privacy criteria. Graph is explored for dataset representation, background knowledge specification, anonymity operation design, as well as attack inferring analysis. The framework is designed to accommodate various datasets including social networks, relational tables, temporal and spatial sequences, and even possible unknown data models. The privacy and utility measurements of the anonymity datasets are also quantified in terms of graph features. Our experiments show that the framework is capable of facilitating privacy protection by different anonymity approaches for various datasets with desirable performance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a graph-based framework for privacy preserving data publication, which is a systematic abstraction of existing anonymity approaches and privacy criteria. Graph is explored for dataset representation, background knowledge specification, anonymity operation design, as well as attack inferring analysis. The framework is designed to accommodate various datasets including social networks, relational tables, temporal and spatial sequences, and even possible unknown data models. The privacy and utility measurements of the anonymity datasets are also quantified in terms of graph features. Our experiments show that the framework is capable of facilitating privacy protection by different anonymity approaches for various datasets with desirable performance.
2020
Ferrari, Elena; Thuraisingham, Bhavani M
Digital Trust: Trust Management in Cyberspace Journal Article
In: IEEE Internet Comput., vol. 24, no. 6, pp. 6–7, 2020.
@article{DBLP:journals/internet/FerrariT20,
title = {Digital Trust: Trust Management in Cyberspace},
author = {Elena Ferrari and Bhavani M Thuraisingham},
url = {https://doi.org/10.1109/MIC.2020.3028898},
doi = {10.1109/MIC.2020.3028898},
year = {2020},
date = {2020-01-01},
journal = {IEEE Internet Comput.},
volume = {24},
number = {6},
pages = {6--7},
abstract = {The four articles in this special section focus on digital trust management in cyberspace. The aim of this special issue is to present some of the most recent advances in trust management for different application domains as well as to discuss new issues and directions for future research and development work. The focus is also on the rise of new technologies, such as Blockchain, which has the potential for being a key pillar to trust management in decentralized settings, by replacing trust in institutions or end users to trust in technology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The four articles in this special section focus on digital trust management in cyberspace. The aim of this special issue is to present some of the most recent advances in trust management for different application domains as well as to discuss new issues and directions for future research and development work. The focus is also on the rise of new technologies, such as Blockchain, which has the potential for being a key pillar to trust management in decentralized settings, by replacing trust in institutions or end users to trust in technology.
Alom, Md. Zulfikar; Carminati, Barbara; Ferrari, Elena
A deep learning model for Twitter spam detection Journal Article
In: Online Soc. Networks Media, vol. 18, pp. 100079, 2020.
@article{DBLP:journals/osnm/AlomCF20,
title = {A deep learning model for Twitter spam detection},
author = {Md. Zulfikar Alom and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1016/j.osnem.2020.100079},
doi = {10.1016/j.osnem.2020.100079},
year = {2020},
date = {2020-01-01},
journal = {Online Soc. Networks Media},
volume = {18},
pages = {100079},
abstract = {Social networking platforms have become a popular way for Internet surfers to meet and interact. Twitter is one of the most popular social networking platforms where users can read the news, share ideas, discuss social issues, as well as stay in touch with friends and families. Due to its huge popularity, it has also become a target for spammers. Until now, researchers have developed many machine learning (ML) based methods for detecting spammers on Twitter. However, the available ML-based methods cannot efficiently detect spammers on Twitter due to possible data manipulations by spam users to avoid detection mechanisms. As an alternative to ML-based detection, in this paper, we present a new approach based on deep learning (DL) techniques. Our approach leverages both on tweet text as well as users’ meta-data (e.g., age of an account, number of followings/followers, and so on) to detect spammers. We compare the performance of the proposed approach with five ML-based and two DL-based state of the art approaches on two different real-world datasets, showing a gain in performance when using our approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Social networking platforms have become a popular way for Internet surfers to meet and interact. Twitter is one of the most popular social networking platforms where users can read the news, share ideas, discuss social issues, as well as stay in touch with friends and families. Due to its huge popularity, it has also become a target for spammers. Until now, researchers have developed many machine learning (ML) based methods for detecting spammers on Twitter. However, the available ML-based methods cannot efficiently detect spammers on Twitter due to possible data manipulations by spam users to avoid detection mechanisms. As an alternative to ML-based detection, in this paper, we present a new approach based on deep learning (DL) techniques. Our approach leverages both on tweet text as well as users’ meta-data (e.g., age of an account, number of followings/followers, and so on) to detect spammers. We compare the performance of the proposed approach with five ML-based and two DL-based state of the art approaches on two different real-world datasets, showing a gain in performance when using our approach.
Rondanini, Christian; Carminati, Barbara; Daidone, Federico; Ferrari, Elena
Blockchain-based controlled information sharing in inter-organizational workflows Proceedings Article
In: 2020 IEEE International Conference on Services Computing, SCC 2020, Beijing, China, November 7-11, 2020, pp. 378–385, IEEE, 2020.
@inproceedings{DBLP:conf/IEEEscc/RondaniniCDF20,
title = {Blockchain-based controlled information sharing in inter-organizational
workflows},
author = {Christian Rondanini and Barbara Carminati and Federico Daidone and Elena Ferrari},
url = {https://doi.org/10.1109/SCC49832.2020.00056},
doi = {10.1109/SCC49832.2020.00056},
year = {2020},
date = {2020-01-01},
booktitle = {2020 IEEE International Conference on Services Computing, SCC
2020, Beijing, China, November 7-11, 2020},
pages = {378--385},
publisher = {IEEE},
abstract = {Nowadays, organizations need to set higher and higher business goals in order to cope with market requirements. Indeed, a widespread strategy for organizations is to join in inter-organizational processes, which set collaborations and resource sharing among involved organizations. However, the possible lack of trust among the organizations poses relevant issues on the processing of sensitive resources. A promising approach to cope with this issue is leveraging on blockchain technology. Thanks to its design and consensus algorithm, blockchain provides a trustworthy infrastructure that allows partners involved in the collaboration to monitor and perform audits on the workflow transitions. In general, the focus of the existing blockchain-based workflow management solutions is mainly workflow coordination. However, a challenging characteristic of some workflows is that they require the exchange of a big amount of data that has to be managed off-chain, that is, directly exchanged between data producer and consumer. This off-chain data sharing should be secured and controlled such to follow the workflow execution.To cope with this challenge, in this paper, we propose a controlled information sharing in inter-organizational workflows enforced via smart contracts. Smart contracts are designed to coordinate the workflow execution, as well as to deploy a set of authorizations granting access only to the task executor and only to those resources needed for task execution and only during the task activation. We have also run a set of experiments to show the feasibility of our approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nowadays, organizations need to set higher and higher business goals in order to cope with market requirements. Indeed, a widespread strategy for organizations is to join in inter-organizational processes, which set collaborations and resource sharing among involved organizations. However, the possible lack of trust among the organizations poses relevant issues on the processing of sensitive resources. A promising approach to cope with this issue is leveraging on blockchain technology. Thanks to its design and consensus algorithm, blockchain provides a trustworthy infrastructure that allows partners involved in the collaboration to monitor and perform audits on the workflow transitions. In general, the focus of the existing blockchain-based workflow management solutions is mainly workflow coordination. However, a challenging characteristic of some workflows is that they require the exchange of a big amount of data that has to be managed off-chain, that is, directly exchanged between data producer and consumer. This off-chain data sharing should be secured and controlled such to follow the workflow execution.To cope with this challenge, in this paper, we propose a controlled information sharing in inter-organizational workflows enforced via smart contracts. Smart contracts are designed to coordinate the workflow execution, as well as to deploy a set of authorizations granting access only to the task executor and only to those resources needed for task execution and only during the task activation. We have also run a set of experiments to show the feasibility of our approach.
Ardagna, Claudio Agostino; Anisetti, Marco; Carminati, Barbara; Damiani, Ernesto; Ferrari, Elena; Rondanini, Christian
A Blockchain-based Trustworthy Certification Process for Composite Services Proceedings Article
In: 2020 IEEE International Conference on Services Computing, SCC 2020, Beijing, China, November 7-11, 2020, pp. 422–429, IEEE, 2020.
@inproceedings{DBLP:conf/IEEEscc/ArdagnaACDFR20,
title = {A Blockchain-based Trustworthy Certification Process for Composite
Services},
author = {Claudio Agostino Ardagna and Marco Anisetti and Barbara Carminati and Ernesto Damiani and Elena Ferrari and Christian Rondanini},
url = {https://doi.org/10.1109/SCC49832.2020.00062},
doi = {10.1109/SCC49832.2020.00062},
year = {2020},
date = {2020-01-01},
booktitle = {2020 IEEE International Conference on Services Computing, SCC
2020, Beijing, China, November 7-11, 2020},
pages = {422--429},
publisher = {IEEE},
abstract = {Lack of trustworthiness is a major limit of microservice-based systems, where service composition is mainly driven by functional requirements. In this paper, we propose an approach where composite service certification meets blockchain, to support continuous and trustworthy verification of non-functional requirements. A certification process for composite services is then introduced at the basis of an audit process aiming to support certificates with stable properties. Trustworthiness is built on the blockchain, used as a platform for coordinating collaboration among involved parties such as service orchestrators, certification authorities, and auditors.},
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Lack of trustworthiness is a major limit of microservice-based systems, where service composition is mainly driven by functional requirements. In this paper, we propose an approach where composite service certification meets blockchain, to support continuous and trustworthy verification of non-functional requirements. A certification process for composite services is then introduced at the basis of an audit process aiming to support certificates with stable properties. Trustworthiness is built on the blockchain, used as a platform for coordinating collaboration among involved parties such as service orchestrators, certification authorities, and auditors.
Hoang, Anh-Tu; Carminati, Barbara; Ferrari, Elena
Cluster-Based Anonymization of Knowledge Graphs Proceedings Article
In: Conti, Mauro; Zhou, Jianying; Casalicchio, Emiliano; Spognardi, Angelo (Ed.): Applied Cryptography and Network Security - 18th International Conference, ACNS 2020, Rome, Italy, October 19-22, 2020, Proceedings, Part II, pp. 104–123, Springer, 2020.
@inproceedings{DBLP:conf/acns/HoangCF20,
title = {Cluster-Based Anonymization of Knowledge Graphs},
author = {Anh-Tu Hoang and Barbara Carminati and Elena Ferrari},
editor = {Mauro Conti and Jianying Zhou and Emiliano Casalicchio and Angelo Spognardi},
url = {https://doi.org/10.1007/978-3-030-57878-7_6},
doi = {10.1007/978-3-030-57878-7_6},
year = {2020},
date = {2020-01-01},
booktitle = {Applied Cryptography and Network Security - 18th International Conference,
ACNS 2020, Rome, Italy, October 19-22, 2020, Proceedings, Part II},
volume = {12147},
pages = {104--123},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
abstract = {While knowledge graphs (KGs) are getting popular as they can formalize many types of users’ data in social networks, sharing these data may reveal users’ identities. Although many protection models have been presented to protect users in anonymized data, they are unsuitable to protect the users in KGs. To cope with this problem, we propose k-AttributeDegree (k-ad), a model to protect users’ identities in anonymized KGs. We further present information loss metrics tailored to KGs and a cluster-based anonymization algorithm to generate anonymized KGs satisfying k-ad. Finally, we conduct experiments on five real-life data sets to evaluate our algorithm and compare it with previous work.},
keywords = {},
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While knowledge graphs (KGs) are getting popular as they can formalize many types of users’ data in social networks, sharing these data may reveal users’ identities. Although many protection models have been presented to protect users in anonymized data, they are unsuitable to protect the users in KGs. To cope with this problem, we propose k-AttributeDegree (k-ad), a model to protect users’ identities in anonymized KGs. We further present information loss metrics tailored to KGs and a cluster-based anonymization algorithm to generate anonymized KGs satisfying k-ad. Finally, we conduct experiments on five real-life data sets to evaluate our algorithm and compare it with previous work.
Colombo, Pietro; Ferrari, Elena; Salvia, Samuele
Mammoth: Monitoring the ABAC Monitor of MQTT-based Internet of Things ecosystems Proceedings Article
In: Lobo, Jorge; Stoller, Scott D; Liu, Peng (Ed.): Proceedings of the 25th ACM Symposium on Access Control Models and Technologies, SACMAT 2020, Barcelona, Spain, June 10-12, 2020, pp. 221–222, ACM, 2020.
@inproceedings{DBLP:conf/sacmat/ColomboFS20,
title = {Mammoth: Monitoring the ABAC Monitor of MQTT-based Internet of Things
ecosystems},
author = {Pietro Colombo and Elena Ferrari and Samuele Salvia},
editor = {Jorge Lobo and Scott D Stoller and Peng Liu},
url = {https://doi.org/10.1145/3381991.3396226},
doi = {10.1145/3381991.3396226},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the 25th ACM Symposium on Access Control Models and
Technologies, SACMAT 2020, Barcelona, Spain, June 10-12, 2020},
pages = {221--222},
publisher = {ACM},
abstract = {Data confidentiality and privacy are becoming primary concerns for Internet of Things applications. A variety of access control approaches have been proposed to address this issue. In this demonstration we present a tool, called Mammoth, which complements an ABAC framework for MQTT-based IoT ecosystems, with a dashboard of analysis services designed for security administrators. Mammoth supports the real-time analysis of target MQTT ecosystems, allowing security administrators to analyze the effects of the enforcement mechanisms on the flow of exchanged messages. The demonstration will allow participants to try Mammoth services in a simulated MQTT-based scenario.},
keywords = {},
pubstate = {published},
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Data confidentiality and privacy are becoming primary concerns for Internet of Things applications. A variety of access control approaches have been proposed to address this issue. In this demonstration we present a tool, called Mammoth, which complements an ABAC framework for MQTT-based IoT ecosystems, with a dashboard of analysis services designed for security administrators. Mammoth supports the real-time analysis of target MQTT ecosystems, allowing security administrators to analyze the effects of the enforcement mechanisms on the flow of exchanged messages. The demonstration will allow participants to try Mammoth services in a simulated MQTT-based scenario.
Anisetti, Marco; Ardagna, Claudio Agostino; Carminati, Barbara; Ferrari, Elena; Perner, Cora Lisa
Requirements and Challenges for Secure and Trustworthy UAS Collaboration Proceedings Article
In: Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020, Atlanta, GA, USA, October 28-31, 2020, pp. 89–98, IEEE, 2020.
@inproceedings{DBLP:conf/tpsisa/AnisettiACFP20,
title = {Requirements and Challenges for Secure and Trustworthy UAS Collaboration},
author = {Marco Anisetti and Claudio Agostino Ardagna and Barbara Carminati and Elena Ferrari and Cora Lisa Perner},
url = {https://doi.org/10.1109/TPS-ISA50397.2020.00022},
doi = {10.1109/TPS-ISA50397.2020.00022},
year = {2020},
date = {2020-01-01},
booktitle = {Second IEEE International Conference on Trust, Privacy and Security
in Intelligent Systems and Applications, TPS-ISA 2020, Atlanta,
GA, USA, October 28-31, 2020},
pages = {89--98},
publisher = {IEEE},
abstract = {Integration and increased uses of unoccupied aerial systems (UAS) challenge current airspace operation. Rather than centralised airspace management (which is rapidly reaching capacity limits), those vehicles need to collaborate safely and efficiently. However, the vehicles differ significantly with respect to capabilities, carried equipment, and certification requirements. The main focus of this paper is how to determine a safe level of interaction in a heterogeneous network, where not all vehicles are (equally) trustworthy, but cooperation is required for many different reasons (e.g., collision avoidance, implementation of collaborative tasks). Consequently, this paper presents the main research challenges deriving from integrating UASs in a shared airspace, with a focus on the demanding scenario of urban air mobility. Specific use cases are described to highlight the main challenges and requirements for a security architecture. Furthermore, a roadmap is presented towards addressing the main challenges: trust estimation, interaction adaptation, controlled information sharing, and continuous monitoring and adaptation.},
keywords = {},
pubstate = {published},
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Integration and increased uses of unoccupied aerial systems (UAS) challenge current airspace operation. Rather than centralised airspace management (which is rapidly reaching capacity limits), those vehicles need to collaborate safely and efficiently. However, the vehicles differ significantly with respect to capabilities, carried equipment, and certification requirements. The main focus of this paper is how to determine a safe level of interaction in a heterogeneous network, where not all vehicles are (equally) trustworthy, but cooperation is required for many different reasons (e.g., collision avoidance, implementation of collaborative tasks). Consequently, this paper presents the main research challenges deriving from integrating UASs in a shared airspace, with a focus on the demanding scenario of urban air mobility. Specific use cases are described to highlight the main challenges and requirements for a security architecture. Furthermore, a roadmap is presented towards addressing the main challenges: trust estimation, interaction adaptation, controlled information sharing, and continuous monitoring and adaptation.
2019
Colombo, Pietro; Ferrari, Elena
Access control technologies for Big Data management systems: literature review and future trends Journal Article
In: Cybersecur., vol. 2, no. 1, pp. 3, 2019.
@article{DBLP:journals/cybersec/ColomboF19,
title = {Access control technologies for Big Data management systems: literature
review and future trends},
author = {Pietro Colombo and Elena Ferrari},
url = {https://doi.org/10.1186/s42400-018-0020-9},
doi = {10.1186/s42400-018-0020-9},
year = {2019},
date = {2019-01-01},
journal = {Cybersecur.},
volume = {2},
number = {1},
pages = {3},
abstract = {Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a reference data model and related data manipulation languages. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a reference data model and related data manipulation languages. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.},
keywords = {},
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Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a reference data model and related data manipulation languages. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a reference data model and related data manipulation languages. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.
Kantarcioglu, Murat; Ferrari, Elena
Research Challenges at the Intersection of Big Data, Security and Privacy Journal Article
In: Frontiers Big Data, vol. 2, pp. 1, 2019.
@article{DBLP:journals/fdata/KantarciogluF19,
title = {Research Challenges at the Intersection of Big Data, Security and
Privacy},
author = {Murat Kantarcioglu and Elena Ferrari},
url = {https://doi.org/10.3389/fdata.2019.00001},
doi = {10.3389/fdata.2019.00001},
year = {2019},
date = {2019-01-01},
journal = {Frontiers Big Data},
volume = {2},
pages = {1},
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Alom, Md. Zulfikar; Carminati, Barbara; Ferrari, Elena
Helping Users Managing Context-Based Privacy Preferences Proceedings Article
In: Bertino, Elisa; Chang, Carl K; Chen, Peter; Damiani, Ernesto; Goul, Michael; Oyama, Katsunori (Ed.): 2019 IEEE International Conference on Services Computing, SCC 2019, Milan, Italy, July 8-13, 2019, pp. 100–107, IEEE, 2019.
@inproceedings{DBLP:conf/IEEEscc/AlomCF19,
title = {Helping Users Managing Context-Based Privacy Preferences},
author = {Md. Zulfikar Alom and Barbara Carminati and Elena Ferrari},
editor = {Elisa Bertino and Carl K Chang and Peter Chen and Ernesto Damiani and Michael Goul and Katsunori Oyama},
url = {https://doi.org/10.1109/SCC.2019.00027},
doi = {10.1109/SCC.2019.00027},
year = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE International Conference on Services Computing, SCC
2019, Milan, Italy, July 8-13, 2019},
pages = {100--107},
publisher = {IEEE},
abstract = {Today, users interact with a variety of online services offered by different providers. In order to supply their services, providers collect, store and process users' data according to their privacy policies. To have more control on personal data, user can specify a set of privacy preferences, encoding the conditions according to which his/her data can be used and managed by the provider. Moreover, many services are context dependent, that is, the type of delivered service is based on user contextual information (e.g., time, location, and so on). This makes more complicated the definition of privacy preferences, as, typically, users might have different attitude with respect the privacy management based on the current context (e.g., working hour, free time). To provide a more fine-grained control, a user can set up different privacy preferences for each different possible contexts. However, since user change the context very frequently, this might result in a very complex and time-consuming task. To cope with this issue, in this paper, we propose a context-based privacy management service that helps users to manage their privacy preferences setting under different contexts. At this aim, we exploit machine learning algorithms to build a classifier, able to infer new privacy preferences for the new context. The preliminary experimental results we have conducted are promising, and show the effectiveness of the proposed approach.},
keywords = {},
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Today, users interact with a variety of online services offered by different providers. In order to supply their services, providers collect, store and process users' data according to their privacy policies. To have more control on personal data, user can specify a set of privacy preferences, encoding the conditions according to which his/her data can be used and managed by the provider. Moreover, many services are context dependent, that is, the type of delivered service is based on user contextual information (e.g., time, location, and so on). This makes more complicated the definition of privacy preferences, as, typically, users might have different attitude with respect the privacy management based on the current context (e.g., working hour, free time). To provide a more fine-grained control, a user can set up different privacy preferences for each different possible contexts. However, since user change the context very frequently, this might result in a very complex and time-consuming task. To cope with this issue, in this paper, we propose a context-based privacy management service that helps users to manage their privacy preferences setting under different contexts. At this aim, we exploit machine learning algorithms to build a classifier, able to infer new privacy preferences for the new context. The preliminary experimental results we have conducted are promising, and show the effectiveness of the proposed approach.
Hoang, Anh-Tu; Carminati, Barbara; Ferrari, Elena
Cluster-Based Anonymization of Directed Graphs Proceedings Article
In: 5th IEEE International Conference on Collaboration and Internet Computing, CIC 2019, Los Angeles, CA, USA, December 12-14, 2019, pp. 91–100, IEEE, 2019.
@inproceedings{DBLP:conf/coinco/HoangCF19,
title = {Cluster-Based Anonymization of Directed Graphs},
author = {Anh-Tu Hoang and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1109/CIC48465.2019.00020},
doi = {10.1109/CIC48465.2019.00020},
year = {2019},
date = {2019-01-01},
booktitle = {5th IEEE International Conference on Collaboration and Internet
Computing, CIC 2019, Los Angeles, CA, USA, December 12-14, 2019},
pages = {91--100},
publisher = {IEEE},
abstract = {Social network providers anonymize graphs storing users' relationships to protect users from being re-identified. Despite the fact that most of the relationships are directed (e.g., follows), few works (e.g., the Paired-degree [1] and K-In\&Out-Degree Anonymity [2]) have been designed to work with directed graphs. In this paper, we show that given a graph, DGA [1]and DSNDG-KIODA [2] are not always able to generate its anonymized version. We overcome this limitation by presenting the Cluster-based Directed Graph Anonymization Algorithm(CDGA) and prove that, by choosing the appropriate parameters, CDGA can generate an anonymized graph satisfying both the Paired k-degree [1] and K-In\&Out-Degree Anonymity [2]. Also, we present the Out-and In-Degree Information Loss Metric to minimize the number of changes made to anonymize the graph. We conduct extensive experiments on three real-life data sets to evaluate the effectiveness of CDGA and compare the quality of the graphs anonymized by CDGA, DGA, and DSNDG-KIODA. The experimental results show that we can generate anonymized graphs, by modifying less than 0.007% of edges in the original graph.},
keywords = {},
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}
Social network providers anonymize graphs storing users' relationships to protect users from being re-identified. Despite the fact that most of the relationships are directed (e.g., follows), few works (e.g., the Paired-degree [1] and K-In&Out-Degree Anonymity [2]) have been designed to work with directed graphs. In this paper, we show that given a graph, DGA [1]and DSNDG-KIODA [2] are not always able to generate its anonymized version. We overcome this limitation by presenting the Cluster-based Directed Graph Anonymization Algorithm(CDGA) and prove that, by choosing the appropriate parameters, CDGA can generate an anonymized graph satisfying both the Paired k-degree [1] and K-In&Out-Degree Anonymity [2]. Also, we present the Out-and In-Degree Information Loss Metric to minimize the number of changes made to anonymize the graph. We conduct extensive experiments on three real-life data sets to evaluate the effectiveness of CDGA and compare the quality of the graphs anonymized by CDGA, DGA, and DSNDG-KIODA. The experimental results show that we can generate anonymized graphs, by modifying less than 0.007% of edges in the original graph.
Rondanini, Christian; Carminati, Barbara; Ferrari, Elena
Confidential Discovery of IoT Devices through Blockchain Proceedings Article
In: Bertino, Elisa; Chang, Carl K; Chen, Peter; Damiani, Ernesto; Goul, Michael; Oyama, Katsunori (Ed.): 2019 IEEE International Congress on Internet of Things, ICIOT 2019, Milan, Italy, July 8-13, 2019, pp. 1–8, IEEE, 2019.
@inproceedings{DBLP:conf/iciot/RondaniniCF19,
title = {Confidential Discovery of IoT Devices through Blockchain},
author = {Christian Rondanini and Barbara Carminati and Elena Ferrari},
editor = {Elisa Bertino and Carl K Chang and Peter Chen and Ernesto Damiani and Michael Goul and Katsunori Oyama},
url = {https://doi.org/10.1109/ICIOT.2019.00014},
doi = {10.1109/ICIOT.2019.00014},
year = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE International Congress on Internet of Things, ICIOT
2019, Milan, Italy, July 8-13, 2019},
pages = {1--8},
publisher = {IEEE},
abstract = {Selection criteria regulating IoT device discovery involve confidentiality issue on the information the constraints convey. A promising approach to cope with this issue is leveraging on blockchain technology and smart contracts to implement the overall discovery process deployment. However, due to the blockchain design, data within the blockchain is public and smart contracts cannot access data outside the blockchain, unless through the exploitation of Oracles. On the one hand, this brings benefits of trust decentralization, transparency, and accountability of the discovery process. On the other hand, it carries serious consequences on confidentiality and privacy as well as on Oracles trustworthiness. For these reasons, in this paper, we investigate how to ensure data confidentiality during the discovery process of IoT devices on blockchain even in the presence of an untrusted Oracle.},
keywords = {},
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Selection criteria regulating IoT device discovery involve confidentiality issue on the information the constraints convey. A promising approach to cope with this issue is leveraging on blockchain technology and smart contracts to implement the overall discovery process deployment. However, due to the blockchain design, data within the blockchain is public and smart contracts cannot access data outside the blockchain, unless through the exploitation of Oracles. On the one hand, this brings benefits of trust decentralization, transparency, and accountability of the discovery process. On the other hand, it carries serious consequences on confidentiality and privacy as well as on Oracles trustworthiness. For these reasons, in this paper, we investigate how to ensure data confidentiality during the discovery process of IoT devices on blockchain even in the presence of an untrusted Oracle.
Alom, Md. Zulfikar; Carminati, Barbara; Ferrari, Elena
Adapting Users' Privacy Preferences in Smart Environments Proceedings Article
In: Bertino, Elisa; Chang, Carl K; Chen, Peter; Damiani, Ernesto; Goul, Michael; Oyama, Katsunori (Ed.): 2019 IEEE International Congress on Internet of Things, ICIOT 2019, Milan, Italy, July 8-13, 2019, pp. 165–172, IEEE, 2019.
@inproceedings{DBLP:conf/iciot/AlomCF19,
title = {Adapting Users' Privacy Preferences in Smart Environments},
author = {Md. Zulfikar Alom and Barbara Carminati and Elena Ferrari},
editor = {Elisa Bertino and Carl K Chang and Peter Chen and Ernesto Damiani and Michael Goul and Katsunori Oyama},
url = {https://doi.org/10.1109/ICIOT.2019.00036},
doi = {10.1109/ICIOT.2019.00036},
year = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE International Congress on Internet of Things, ICIOT
2019, Milan, Italy, July 8-13, 2019},
pages = {165--172},
publisher = {IEEE},
abstract = {A smart environment is a physical space where devices are connected to provide continuous support to individuals and make their life more comfortable. For this purpose, a smart environment collects, stores, and processes a massive amount of personal data. In general, service providers collect these data according to their privacy policies. To enhance the privacy control, individuals can explicitly express their privacy preferences, stating conditions on how their data have to be used and managed. Typically, privacy checking is handled through the hard matching of users’ privacy preferences against service providers’ privacy policies, by denying all service requests whose privacy policies do not fully match with individual’s privacy preferences. However, this hard matching might be too restrictive in a smart environment because it denies the services that partially satisfy the individual’s privacy preferences. To cope with this challenge, in this paper, we propose a soft privacy matching mechanism, able to relax, in a controlled way, some conditions of users’ privacy preferences such to match with service providers’ privacy policies. At this aim, we exploit machine learning algorithms to build a classifier, which is able to make decisions on future service requests, by learning which privacy preference components a user is prone to relax, as well as the relaxation tolerance. We test our approach on two realistic datasets, obtaining promising results.},
keywords = {},
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}
A smart environment is a physical space where devices are connected to provide continuous support to individuals and make their life more comfortable. For this purpose, a smart environment collects, stores, and processes a massive amount of personal data. In general, service providers collect these data according to their privacy policies. To enhance the privacy control, individuals can explicitly express their privacy preferences, stating conditions on how their data have to be used and managed. Typically, privacy checking is handled through the hard matching of users’ privacy preferences against service providers’ privacy policies, by denying all service requests whose privacy policies do not fully match with individual’s privacy preferences. However, this hard matching might be too restrictive in a smart environment because it denies the services that partially satisfy the individual’s privacy preferences. To cope with this challenge, in this paper, we propose a soft privacy matching mechanism, able to relax, in a controlled way, some conditions of users’ privacy preferences such to match with service providers’ privacy policies. At this aim, we exploit machine learning algorithms to build a classifier, which is able to make decisions on future service requests, by learning which privacy preference components a user is prone to relax, as well as the relaxation tolerance. We test our approach on two realistic datasets, obtaining promising results.
2018
Sagirlar, Gokhan; Carminati, Barbara; Ferrari, Elena
Decentralizing privacy enforcement for Internet of Things smart objects Journal Article
In: Comput. Networks, vol. 143, pp. 112–125, 2018.
@article{DBLP:journals/cn/SagirlarCF18,
title = {Decentralizing privacy enforcement for Internet of Things smart objects},
author = {Gokhan Sagirlar and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1016/j.comnet.2018.07.019},
doi = {10.1016/j.comnet.2018.07.019},
year = {2018},
date = {2018-01-01},
journal = {Comput. Networks},
volume = {143},
pages = {112--125},
abstract = {Internet of Things (IoT) is now evolving into a loosely coupled, decentralized system of cooperating smart objects, where high-speed data processing, analytics and shorter response times are becoming more necessary than ever. Such decentralization has a great impact on the way personal information generated and consumed by smart objects should be protected, because, without centralized data management, it is more difficult to control how data are combined and used by smart objects. To cope with this issue, in this paper, we propose a framework where users of smart objects can specify their privacy preferences. Compliance check of user individual privacy preferences is performed directly by smart objects. Moreover, acknowledging that embedding the enforcement mechanism into smart objects implies some overhead, we have extensively tested the proposed framework on different scenarios, and the obtained results show the feasibility of our approach.},
keywords = {},
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}
Internet of Things (IoT) is now evolving into a loosely coupled, decentralized system of cooperating smart objects, where high-speed data processing, analytics and shorter response times are becoming more necessary than ever. Such decentralization has a great impact on the way personal information generated and consumed by smart objects should be protected, because, without centralized data management, it is more difficult to control how data are combined and used by smart objects. To cope with this issue, in this paper, we propose a framework where users of smart objects can specify their privacy preferences. Compliance check of user individual privacy preferences is performed directly by smart objects. Moreover, acknowledging that embedding the enforcement mechanism into smart objects implies some overhead, we have extensively tested the proposed framework on different scenarios, and the obtained results show the feasibility of our approach.
Bahri, Leila; Carminati, Barbara; Ferrari, Elena
Decentralized privacy preserving services for Online Social Networks Journal Article
In: Online Soc. Networks Media, vol. 6, pp. 18–25, 2018.
@article{DBLP:journals/osnm/BahriCF18,
title = {Decentralized privacy preserving services for Online Social Networks},
author = {Leila Bahri and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1016/j.osnem.2018.02.001},
doi = {10.1016/j.osnem.2018.02.001},
year = {2018},
date = {2018-01-01},
journal = {Online Soc. Networks Media},
volume = {6},
pages = {18--25},
abstract = {Current popular and widely adopted Online Social Networks (OSNs) all follow a logically centered architecture, by which one single entity owns unprecedented collections of personal data in terms of amount, variety, geographical span, and richness in detail. This is clearly constituting one of the major threats to users privacy and to their right to be-left-alone. Decentralization has then been considered as the panacea to privacy issues, especially in the realms of OSNs. However, with a more thoughtful consideration of the issue, it could be argued that decentralization, if not designed and implemented carefully and properly, can have more serious implications on users privacy rather than bringing radical solutions. Moreover, research on Decentralized Online Social Networks (DOSNs) has shown that there are more challenges to their realization that need proper attention and more innovative technical solutions. In this paper, we discuss the issues related to privacy preservation between centralization and decentralization, and we provide a review of available research work on decentralized privacy preserving services for social networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Current popular and widely adopted Online Social Networks (OSNs) all follow a logically centered architecture, by which one single entity owns unprecedented collections of personal data in terms of amount, variety, geographical span, and richness in detail. This is clearly constituting one of the major threats to users privacy and to their right to be-left-alone. Decentralization has then been considered as the panacea to privacy issues, especially in the realms of OSNs. However, with a more thoughtful consideration of the issue, it could be argued that decentralization, if not designed and implemented carefully and properly, can have more serious implications on users privacy rather than bringing radical solutions. Moreover, research on Decentralized Online Social Networks (DOSNs) has shown that there are more challenges to their realization that need proper attention and more innovative technical solutions. In this paper, we discuss the issues related to privacy preservation between centralization and decentralization, and we provide a review of available research work on decentralized privacy preserving services for social networks.
Laleh, Naeimeh; Carminati, Barbara; Ferrari, Elena
Risk Assessment in Social Networks Based on User Anomalous Behaviors Journal Article
In: IEEE Trans. Dependable Secur. Comput., vol. 15, no. 2, pp. 295–308, 2018.
@article{DBLP:journals/tdsc/LalehCF18,
title = {Risk Assessment in Social Networks Based on User Anomalous Behaviors},
author = {Naeimeh Laleh and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1109/TDSC.2016.2540637},
doi = {10.1109/TDSC.2016.2540637},
year = {2018},
date = {2018-01-01},
journal = {IEEE Trans. Dependable Secur. Comput.},
volume = {15},
number = {2},
pages = {295--308},
abstract = {Although the dramatic increase in Online Social Network (OSN) usage, there are still a lot of security and privacy concerns. In such a scenario, it would be very beneficial to have a mechanism able to assign a risk score to each OSN user. For this reason, in this paper, we propose a risk assessment based on the idea that the more a user behavior diverges from what it can be considered as a `normal behavior', the more it should be considered risky. In doing this, we have taken into account that OSN population is really heterogeneous in observed behaviors. As such, it is not possible to define a unique standard behavioral model that fits all OSN users' behaviors. However, we expect that similar people tend to follow similar rules with the results of similar behavioral models. For this reason, we propose a risk assessment approach organized into two phases: similar users are first grouped together, then, for each identified group, we build one or more models for normal behavior. The carried out experiments on a real Facebook dataset show that the proposed model outperforms a simplified behavioral-based risk assessment where behavioral models are built over the whole OSN population, without a group identification phase.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Although the dramatic increase in Online Social Network (OSN) usage, there are still a lot of security and privacy concerns. In such a scenario, it would be very beneficial to have a mechanism able to assign a risk score to each OSN user. For this reason, in this paper, we propose a risk assessment based on the idea that the more a user behavior diverges from what it can be considered as a `normal behavior', the more it should be considered risky. In doing this, we have taken into account that OSN population is really heterogeneous in observed behaviors. As such, it is not possible to define a unique standard behavioral model that fits all OSN users' behaviors. However, we expect that similar people tend to follow similar rules with the results of similar behavioral models. For this reason, we propose a risk assessment approach organized into two phases: similar users are first grouped together, then, for each identified group, we build one or more models for normal behavior. The carried out experiments on a real Facebook dataset show that the proposed model outperforms a simplified behavioral-based risk assessment where behavioral models are built over the whole OSN population, without a group identification phase.
Bahri, Leila; Carminati, Barbara; Ferrari, Elena; Bianco, Andrea
Enhanced Audit Strategies for Collaborative and Accountable Data Sharing in Social Networks Journal Article
In: ACM Trans. Internet Techn., vol. 18, no. 4, pp. 44:1–44:19, 2018.
@article{DBLP:journals/toit/BahriCFB18,
title = {Enhanced Audit Strategies for Collaborative and Accountable Data Sharing
in Social Networks},
author = {Leila Bahri and Barbara Carminati and Elena Ferrari and Andrea Bianco},
url = {https://doi.org/10.1145/3134439},
doi = {10.1145/3134439},
year = {2018},
date = {2018-01-01},
journal = {ACM Trans. Internet Techn.},
volume = {18},
number = {4},
pages = {44:1--44:19},
abstract = {Data sharing and access control management is one of the issues still hindering the development of decentralized online social networks (DOSNs), which are now gaining more research attention with the recent developments in P2P computing, such as the secure public ledger\textendashbased protocols (Blockchains) for monetary systems. In a previous work, we proposed an initial audit\textendashbased model for access control in DOSNs. In this article, we focus on enhancing the audit strategies and the privacy issues emerging from records kept for audit purposes. We propose enhanced audit and collaboration strategies, for which experimental results, on a real online social network graph with simulated sharing behavior, show an improvement in the detection rate of bad behavior of more than 50% compared to the basic model. We also provide an analysis of the related privacy issues and discuss possible privacy-preserving alternatives.Data sharing and access control management is one of the issues still hindering the development of decentralized online social networks (DOSNs), which are now gaining more research attention with the recent developments in P2P computing, such as the secure public ledger\textendashbased protocols (Blockchains) for monetary systems. In a previous work, we proposed an initial audit\textendashbased model for access control in DOSNs. In this article, we focus on enhancing the audit strategies and the privacy issues emerging from records kept for audit purposes. We propose enhanced audit and collaboration strategies, for which experimental results, on a real online social network graph with simulated sharing behavior, show an improvement in the detection rate of bad behavior of more than 50% compared to the basic model. We also provide an analysis of the related privacy issues and discuss possible privacy-preserving alternatives.},
keywords = {},
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Data sharing and access control management is one of the issues still hindering the development of decentralized online social networks (DOSNs), which are now gaining more research attention with the recent developments in P2P computing, such as the secure public ledger–based protocols (Blockchains) for monetary systems. In a previous work, we proposed an initial audit–based model for access control in DOSNs. In this article, we focus on enhancing the audit strategies and the privacy issues emerging from records kept for audit purposes. We propose enhanced audit and collaboration strategies, for which experimental results, on a real online social network graph with simulated sharing behavior, show an improvement in the detection rate of bad behavior of more than 50% compared to the basic model. We also provide an analysis of the related privacy issues and discuss possible privacy-preserving alternatives.Data sharing and access control management is one of the issues still hindering the development of decentralized online social networks (DOSNs), which are now gaining more research attention with the recent developments in P2P computing, such as the secure public ledger–based protocols (Blockchains) for monetary systems. In a previous work, we proposed an initial audit–based model for access control in DOSNs. In this article, we focus on enhancing the audit strategies and the privacy issues emerging from records kept for audit purposes. We propose enhanced audit and collaboration strategies, for which experimental results, on a real online social network graph with simulated sharing behavior, show an improvement in the detection rate of bad behavior of more than 50% compared to the basic model. We also provide an analysis of the related privacy issues and discuss possible privacy-preserving alternatives.
Bahri, Leila; Carminati, Barbara; Ferrari, Elena
Privacy in Web Service Transactions: A Tale of More than a Decade of Work Journal Article
In: IEEE Trans. Serv. Comput., vol. 11, no. 2, pp. 448–465, 2018.
@article{DBLP:journals/tsc/BahriCF18,
title = {Privacy in Web Service Transactions: A Tale of More than a Decade
of Work},
author = {Leila Bahri and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1109/TSC.2017.2711019},
doi = {10.1109/TSC.2017.2711019},
year = {2018},
date = {2018-01-01},
journal = {IEEE Trans. Serv. Comput.},
volume = {11},
number = {2},
pages = {448--465},
abstract = {The web service computing paradigm has introduced great benefits to the growth of e-markets, both under the customer to business and the business to business models. The value capabilities allowed by the conception of web services, such as interoperability, efficiency, just-in-time integration, etc., have made them the most common way of doing business online. With the maturation of the web services underlying functional properties and facilitating standards, and with the proliferation of the amounts of data they use and they generate, researchers and practitioners have been dedicating considerable efforts to the related emerging privacy concerns. The literature contains number of research works on these privacy concerns, each addressing them from a different focal point. We have explored the available literature on web services privacy during transactions, to present, in this paper, a thorough survey of the most relevant published proposals. We identified 20 works that address privacy related problems in web services consumption. We categorize them based on the approach they take and we compare them based on a proposed evaluation framework, derived from the adopted techniques and addressed requirements.},
keywords = {},
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The web service computing paradigm has introduced great benefits to the growth of e-markets, both under the customer to business and the business to business models. The value capabilities allowed by the conception of web services, such as interoperability, efficiency, just-in-time integration, etc., have made them the most common way of doing business online. With the maturation of the web services underlying functional properties and facilitating standards, and with the proliferation of the amounts of data they use and they generate, researchers and practitioners have been dedicating considerable efforts to the related emerging privacy concerns. The literature contains number of research works on these privacy concerns, each addressing them from a different focal point. We have explored the available literature on web services privacy during transactions, to present, in this paper, a thorough survey of the most relevant published proposals. We identified 20 works that address privacy related problems in web services consumption. We categorize them based on the approach they take and we compare them based on a proposed evaluation framework, derived from the adopted techniques and addressed requirements.
Bahri, Leila; Carminati, Barbara; Ferrari, Elena
Knowledge-based approaches for identity management in online social networks Journal Article
In: Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 8, no. 5, 2018.
@article{DBLP:journals/widm/BahriCF18,
title = {Knowledge-based approaches for identity management in online social
networks},
author = {Leila Bahri and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1002/widm.1260},
doi = {10.1002/widm.1260},
year = {2018},
date = {2018-01-01},
journal = {Wiley Interdiscip. Rev. Data Min. Knowl. Discov.},
volume = {8},
number = {5},
abstract = {When we meet a new person, we start by introducing ourselves. We share our names, and other information about our jobs, cities, family status, and so on. This is how socializing and social interactions can start: we first need to identify each other. Identification is a cornerstone in establishing social contacts. We identify ourselves and others by a set of civil (e.g., name, nationality, ID number, gender) and social (e.g., music taste, hobbies, religion) characteristics. This seamlessly carried out identification process in face-to-face interactions is challenged in the virtual realms of socializing, such as in online social network (OSN) platforms. New identities (i.e., online profiles) could be created without being subject to any level of verification, making it easy to create fake information and forge fake identities. This has led to a massive proliferation of accounts that represent fake identities (i.e., not mapping to physically existing entities), and that poison the online socializing environment with fake information and malicious behavior (e.g., child abuse, information stealing). Within this milieu, users in OSNs are left unarmed against the challenging task of identifying the real person behind the screen. OSN providers and research bodies have dedicated considerable effort to the study of the behavior and features of fake OSN identities, trying to find ways to detect them. Some other research initiatives have explored possible techniques to enable identity validation in OSNs. Both kinds of approach rely on extracting knowledge from the OSN, and exploiting it to achieve identification management in their realms. We provide a review of the most prominent works in the literature. We define the problem, provide a taxonomy of related attacks, and discuss the available solutions and approaches for knowledge-based identity management in OSNs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
When we meet a new person, we start by introducing ourselves. We share our names, and other information about our jobs, cities, family status, and so on. This is how socializing and social interactions can start: we first need to identify each other. Identification is a cornerstone in establishing social contacts. We identify ourselves and others by a set of civil (e.g., name, nationality, ID number, gender) and social (e.g., music taste, hobbies, religion) characteristics. This seamlessly carried out identification process in face-to-face interactions is challenged in the virtual realms of socializing, such as in online social network (OSN) platforms. New identities (i.e., online profiles) could be created without being subject to any level of verification, making it easy to create fake information and forge fake identities. This has led to a massive proliferation of accounts that represent fake identities (i.e., not mapping to physically existing entities), and that poison the online socializing environment with fake information and malicious behavior (e.g., child abuse, information stealing). Within this milieu, users in OSNs are left unarmed against the challenging task of identifying the real person behind the screen. OSN providers and research bodies have dedicated considerable effort to the study of the behavior and features of fake OSN identities, trying to find ways to detect them. Some other research initiatives have explored possible techniques to enable identity validation in OSNs. Both kinds of approach rely on extracting knowledge from the OSN, and exploiting it to achieve identification management in their realms. We provide a review of the most prominent works in the literature. We define the problem, provide a taxonomy of related attacks, and discuss the available solutions and approaches for knowledge-based identity management in OSNs.
Alom, Md. Zulfikar; Carminati, Barbara; Ferrari, Elena
Detecting Spam Accounts on Twitter Proceedings Article
In: Brandes, Ulrik; Reddy, Chandan; Tagarelli, Andrea (Ed.): IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Barcelona, Spain, August 28-31, 2018, pp. 1191–1198, IEEE Computer Society, 2018.
@inproceedings{DBLP:conf/asunam/AlomCF18,
title = {Detecting Spam Accounts on Twitter},
author = {Md. Zulfikar Alom and Barbara Carminati and Elena Ferrari},
editor = {Ulrik Brandes and Chandan Reddy and Andrea Tagarelli},
url = {https://doi.org/10.1109/ASONAM.2018.8508495},
doi = {10.1109/ASONAM.2018.8508495},
year = {2018},
date = {2018-01-01},
booktitle = {IEEE/ACM 2018 International Conference on Advances in Social Networks
Analysis and Mining, ASONAM 2018, Barcelona, Spain, August 28-31,
2018},
pages = {1191--1198},
publisher = {IEEE Computer Society},
abstract = {Social networks have become a popular way for internet surfers to interact with friends and family members, reading news, and also discuss events. Users spend more time on well-known social platforms (e.g., Facebook, Twitter, etc.) storing and sharing their personal information. This information together with the opportunity of contacting thousands of users attract the interest of malicious users. They exploit the implicit trust relationships between users in order to achieve their malicious aims, for example, create malicious links within the posts/tweets, spread fake news, send out unsolicited messages to legitimate users, etc. In this paper, we investigate the nature of spam users on Twitter with the goal to improve existing spam detection mechanisms. For detecting Twitter spammers, we make use of several new features, which are more effective and robust than existing used features (e.g., number of followings/followers, etc.). We evaluated the proposed set of features by exploiting very popular machine learning classification algorithms, namely k-Nearest Neighbor (k-NN), Decision Tree (DT), Naive Bayesian (NB), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XG-Boost). The performance of these classifiers are evaluated and compared based on different evaluation metrics. We compared the performance of our proposed approach with four latest state of art approaches. The experimental results show that the proposed set of features gives better performance than existing state of art approaches.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Social networks have become a popular way for internet surfers to interact with friends and family members, reading news, and also discuss events. Users spend more time on well-known social platforms (e.g., Facebook, Twitter, etc.) storing and sharing their personal information. This information together with the opportunity of contacting thousands of users attract the interest of malicious users. They exploit the implicit trust relationships between users in order to achieve their malicious aims, for example, create malicious links within the posts/tweets, spread fake news, send out unsolicited messages to legitimate users, etc. In this paper, we investigate the nature of spam users on Twitter with the goal to improve existing spam detection mechanisms. For detecting Twitter spammers, we make use of several new features, which are more effective and robust than existing used features (e.g., number of followings/followers, etc.). We evaluated the proposed set of features by exploiting very popular machine learning classification algorithms, namely k-Nearest Neighbor (k-NN), Decision Tree (DT), Naive Bayesian (NB), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XG-Boost). The performance of these classifiers are evaluated and compared based on different evaluation metrics. We compared the performance of our proposed approach with four latest state of art approaches. The experimental results show that the proposed set of features gives better performance than existing state of art approaches.
Sagirlar, Gokhan; Carminati, Barbara; Ferrari, Elena
AutoBotCatcher: Blockchain-Based P2P Botnet Detection for the Internet of Things Proceedings Article
In: 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018, Philadelphia, PA, USA, October 18-20, 2018, pp. 1–8, IEEE Computer Society, 2018.
@inproceedings{DBLP:conf/coinco/SagirlarCF18,
title = {AutoBotCatcher: Blockchain-Based P2P Botnet Detection for the Internet
of Things},
author = {Gokhan Sagirlar and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1109/CIC.2018.00-46},
doi = {10.1109/CIC.2018.00-46},
year = {2018},
date = {2018-01-01},
booktitle = {4th IEEE International Conference on Collaboration and Internet
Computing, CIC 2018, Philadelphia, PA, USA, October 18-20, 2018},
pages = {1--8},
publisher = {IEEE Computer Society},
abstract = {In general, a botnet is a collection of compromised internet computers, controlled by attackers for malicious purposes. To increase attacks' success chance and resilience against defence mechanisms, modern botnets have often a decentralized P2P structure. Here, IoT devices are playing a critical role, becoming one of the major tools for malicious parties to perform attacks. Notable examples are DDoS attacks on Krebs on Security and DYN, which have been performed by IoT devices part of botnets. We take a first step towards detecting P2P botnets in IoT, by proposing AutoBotCatcher, whose design is driven by the consideration that bots of the same botnet frequently communicate with each other and form communities. As such, the purpose of AutoBotCatcher is to dynamically analyze communities of IoT devices, formed according to their network traffic flows, to detect botnets. AutoBotCatcher exploits a Byzantine Fault Tolerant (BFT) blockchain, as a state transition machine that allows collaboration of multiple parties without trust, in order to perform collaborative and dynamic botnet detection by collecting and auditing IoT devices' network traffic flows as blockchain transactions. In this paper, we focus on the design of the AutoBotCatcher by first defining the blockchain structure underlying AutoBot-Catcher, then discussing its components.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In general, a botnet is a collection of compromised internet computers, controlled by attackers for malicious purposes. To increase attacks' success chance and resilience against defence mechanisms, modern botnets have often a decentralized P2P structure. Here, IoT devices are playing a critical role, becoming one of the major tools for malicious parties to perform attacks. Notable examples are DDoS attacks on Krebs on Security and DYN, which have been performed by IoT devices part of botnets. We take a first step towards detecting P2P botnets in IoT, by proposing AutoBotCatcher, whose design is driven by the consideration that bots of the same botnet frequently communicate with each other and form communities. As such, the purpose of AutoBotCatcher is to dynamically analyze communities of IoT devices, formed according to their network traffic flows, to detect botnets. AutoBotCatcher exploits a Byzantine Fault Tolerant (BFT) blockchain, as a state transition machine that allows collaboration of multiple parties without trust, in order to perform collaborative and dynamic botnet detection by collecting and auditing IoT devices' network traffic flows as blockchain transactions. In this paper, we focus on the design of the AutoBotCatcher by first defining the blockchain structure underlying AutoBot-Catcher, then discussing its components.
Carminati, Barbara; Ferrari, Elena; Rondanini, Christian
Blockchain as a Platform for Secure Inter-Organizational Business Processes Proceedings Article
In: 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018, Philadelphia, PA, USA, October 18-20, 2018, pp. 122–129, IEEE Computer Society, 2018.
@inproceedings{DBLP:conf/coinco/CarminatiFR18,
title = {Blockchain as a Platform for Secure Inter-Organizational Business
Processes},
author = {Barbara Carminati and Elena Ferrari and Christian Rondanini},
url = {https://doi.org/10.1109/CIC.2018.00027},
doi = {10.1109/CIC.2018.00027},
year = {2018},
date = {2018-01-01},
booktitle = {4th IEEE International Conference on Collaboration and Internet
Computing, CIC 2018, Philadelphia, PA, USA, October 18-20, 2018},
pages = {122--129},
publisher = {IEEE Computer Society},
abstract = {Today, most of the services one may think of are based on a collaborative paradigm (e.g., social media services, IoT-based services, etc.). One of the most relevant representative of such class of services are inter-organizational processes, where an organized group of joined activities is carried out by two or more organizations to achieve a common business goal. Inter-organizational processes are therefore vital to achieve business partnerships among different organizations. However, they may also pose serious security and privacy threats to the data each organization exposes. This is mainly due to the weak trust relationships that may hold among the collaborating parties, which result in a potential lack of trust on how data/operations are managed. In this paper, we discuss, how blockchain, one of today hottest technology, can be used in support of secure inter-organizational processes. We further point out which additional security issues the use of blockchain can bring, illustrate the ongoing research projects in the area and discuss future research directions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Today, most of the services one may think of are based on a collaborative paradigm (e.g., social media services, IoT-based services, etc.). One of the most relevant representative of such class of services are inter-organizational processes, where an organized group of joined activities is carried out by two or more organizations to achieve a common business goal. Inter-organizational processes are therefore vital to achieve business partnerships among different organizations. However, they may also pose serious security and privacy threats to the data each organization exposes. This is mainly due to the weak trust relationships that may hold among the collaborating parties, which result in a potential lack of trust on how data/operations are managed. In this paper, we discuss, how blockchain, one of today hottest technology, can be used in support of secure inter-organizational processes. We further point out which additional security issues the use of blockchain can bring, illustrate the ongoing research projects in the area and discuss future research directions.
Carminati, Barbara; Rondanini, Christian; Ferrari, Elena
Confidential Business Process Execution on Blockchain Proceedings Article
In: 2018 IEEE International Conference on Web Services, ICWS 2018, San Francisco, CA, USA, July 2-7, 2018, pp. 58–65, IEEE, 2018.
@inproceedings{DBLP:conf/icws/CarminatiRF18,
title = {Confidential Business Process Execution on Blockchain},
author = {Barbara Carminati and Christian Rondanini and Elena Ferrari},
url = {https://doi.org/10.1109/ICWS.2018.00015},
doi = {10.1109/ICWS.2018.00015},
year = {2018},
date = {2018-01-01},
booktitle = {2018 IEEE International Conference on Web Services, ICWS 2018,
San Francisco, CA, USA, July 2-7, 2018},
pages = {58--65},
publisher = {IEEE},
abstract = {One of the main issues in service collaborations among business partners is the possible lack of trust among them. A promising approach to cope with this issue is leveraging on blockchain technology by encoding with smart contracts the business process workflow. This brings the benefits of trust decentralization, transparency, and accountability of the service composition process. However, data in the blockchain are public, implying thus serious consequences on confidentiality and privacy. Moreover, smart contracts can access data outside the blockchain only through Oracles, which might pose new confidentiality risks if no assumptions are made on their trustworthiness. For these reasons, in this paper, we are interested in investigating how to ensure data confidentiality during business process execution on blockchain even in the presence of an untrusted Oracle.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the main issues in service collaborations among business partners is the possible lack of trust among them. A promising approach to cope with this issue is leveraging on blockchain technology by encoding with smart contracts the business process workflow. This brings the benefits of trust decentralization, transparency, and accountability of the service composition process. However, data in the blockchain are public, implying thus serious consequences on confidentiality and privacy. Moreover, smart contracts can access data outside the blockchain only through Oracles, which might pose new confidentiality risks if no assumptions are made on their trustworthiness. For these reasons, in this paper, we are interested in investigating how to ensure data confidentiality during business process execution on blockchain even in the presence of an untrusted Oracle.
Sagirlar, Gokhan; Carminati, Barbara; Ferrari, Elena; Sheehan, John D; Ragnoli, Emanuele
Hybrid-IoT: Hybrid Blockchain Architecture for Internet of Things - PoW Sub-Blockchains Proceedings Article
In: IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), iThings/GreenCom/CPSCom/SmartData 2018, Halifax, NS, Canada, July 30 - August 3, 2018, pp. 1007–1016, IEEE, 2018.
@inproceedings{DBLP:conf/ithings/SagirlarCFSR18,
title = {Hybrid-IoT: Hybrid Blockchain Architecture for Internet of Things
- PoW Sub-Blockchains},
author = {Gokhan Sagirlar and Barbara Carminati and Elena Ferrari and John D Sheehan and Emanuele Ragnoli},
url = {https://doi.org/10.1109/Cybermatics_2018.2018.00189},
doi = {10.1109/Cybermatics_2018.2018.00189},
year = {2018},
date = {2018-01-01},
booktitle = {IEEE International Conference on Internet of Things (iThings) and
IEEE Green Computing and Communications (GreenCom) and IEEE Cyber,
Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData),
iThings/GreenCom/CPSCom/SmartData 2018, Halifax, NS, Canada, July
30 - August 3, 2018},
pages = {1007--1016},
publisher = {IEEE},
abstract = {From its early days the Internet of Things (IoT) has evolved into a decentralized system of cooperating smart objects with the requirement, among others, of achieving distributed consensus. Yet, current IoT platform solutions are centralized cloud based computing infrastructures, manifesting a number of significant disadvantages, such as, among others, high cloud server maintenance costs, weakness for supporting time-critical IoT applications, security and trust issues. Enabling blockchain technology into IoT can help to achieve a proper distributed consensus based IoT system that overcomes those disadvantages. While this is an ideal match, it is still a challenging endeavor. In this paper we take a first step towards that goal by designing Hybrid-IoT, a hybrid blockchain architecture for IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Then, the connection among the PoW subblockchains employs a BFT inter-connector framework, such as Polkadot or Cosmos. In this paper, we focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds. In order to prove the validity of the approach we carry on a performance and security evaluation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
From its early days the Internet of Things (IoT) has evolved into a decentralized system of cooperating smart objects with the requirement, among others, of achieving distributed consensus. Yet, current IoT platform solutions are centralized cloud based computing infrastructures, manifesting a number of significant disadvantages, such as, among others, high cloud server maintenance costs, weakness for supporting time-critical IoT applications, security and trust issues. Enabling blockchain technology into IoT can help to achieve a proper distributed consensus based IoT system that overcomes those disadvantages. While this is an ideal match, it is still a challenging endeavor. In this paper we take a first step towards that goal by designing Hybrid-IoT, a hybrid blockchain architecture for IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Then, the connection among the PoW subblockchains employs a BFT inter-connector framework, such as Polkadot or Cosmos. In this paper, we focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds. In order to prove the validity of the approach we carry on a performance and security evaluation.
Colombo, Pietro; Ferrari, Elena
Access Control in the Era of Big Data: State of the Art and Research Directions Proceedings Article
In: Bertino, Elisa; Lin, Dan; Lobo, Jorge (Ed.): Proceedings of the 23nd ACM on Symposium on Access Control Models and Technologies, SACMAT 2018, Indianapolis, IN, USA, June 13-15, 2018, pp. 185–192, ACM, 2018.
@inproceedings{DBLP:conf/sacmat/ColomboF18,
title = {Access Control in the Era of Big Data: State of the Art and Research
Directions},
author = {Pietro Colombo and Elena Ferrari},
editor = {Elisa Bertino and Dan Lin and Jorge Lobo},
url = {https://doi.org/10.1145/3205977.3205998},
doi = {10.1145/3205977.3205998},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 23nd ACM on Symposium on Access Control Models
and Technologies, SACMAT 2018, Indianapolis, IN, USA, June 13-15,
2018},
pages = {185--192},
publisher = {ACM},
abstract = {Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a standard data model and related data manipulation language. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a standard data model and related data manipulation language. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.
Colombo, Pietro; Ferrari, Elena
Access Control Enforcement within MQTT-based Internet of Things Ecosystems Proceedings Article
In: Bertino, Elisa; Lin, Dan; Lobo, Jorge (Ed.): Proceedings of the 23nd ACM on Symposium on Access Control Models and Technologies, SACMAT 2018, Indianapolis, IN, USA, June 13-15, 2018, pp. 223–234, ACM, 2018.
@inproceedings{DBLP:conf/sacmat/ColomboF18a,
title = {Access Control Enforcement within MQTT-based Internet of Things Ecosystems},
author = {Pietro Colombo and Elena Ferrari},
editor = {Elisa Bertino and Dan Lin and Jorge Lobo},
url = {https://doi.org/10.1145/3205977.3205986},
doi = {10.1145/3205977.3205986},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 23nd ACM on Symposium on Access Control Models
and Technologies, SACMAT 2018, Indianapolis, IN, USA, June 13-15,
2018},
pages = {223--234},
publisher = {ACM},
abstract = {Confidentiality and privacy of data managed by IoT ecosystems is becoming a primary concern. This paper targets the design of a general access control enforcement mechanism for MQTT-based IoT ecosystems. The proposed approach is presented with ABAC, but other access control models can be similarly supported. The solution is based on an enforcement monitor that has been designed to operate as a proxy between MQTT clients and an MQTT server. The monitor enforces access control constraints by intercepting and possibly manipulating the flow of exchanged MQTT control packets. Early experimental evaluations have overall shown low enforcement overhead.},
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Confidentiality and privacy of data managed by IoT ecosystems is becoming a primary concern. This paper targets the design of a general access control enforcement mechanism for MQTT-based IoT ecosystems. The proposed approach is presented with ABAC, but other access control models can be similarly supported. The solution is based on an enforcement monitor that has been designed to operate as a proxy between MQTT clients and an MQTT server. The monitor enforces access control constraints by intercepting and possibly manipulating the flow of exchanged MQTT control packets. Early experimental evaluations have overall shown low enforcement overhead.
Bertino, Elisa; Ferrari, Elena
Big Data Security and Privacy Book Section
In: Flesca, Sergio; Greco, Sergio; Masciari, Elio; à, Domenico Sacc (Ed.): A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, vol. 31, pp. 425–439, Springer International Publishing, 2018.
@incollection{DBLP:books/sp/18/BertinoF18,
title = {Big Data Security and Privacy},
author = {Elisa Bertino and Elena Ferrari},
editor = {Sergio Flesca and Sergio Greco and Elio Masciari and Domenico Sacc \`{a}},
url = {https://doi.org/10.1007/978-3-319-61893-7_25},
doi = {10.1007/978-3-319-61893-7_25},
year = {2018},
date = {2018-01-01},
booktitle = {A Comprehensive Guide Through the Italian Database Research Over the
Last 25 Years},
volume = {31},
pages = {425--439},
publisher = {Springer International Publishing},
series = {Studies in Big Data},
abstract = {This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.},
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This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.
Ferrari, Elena; Baldi, Marco; Baldoni, Roberto (Ed.)
Proceedings of the Second Italian Conference on Cyber Security, Milan, Italy, February 6th - to - 9th, 2018 Proceedings
CEUR-WS.org, vol. 2058, 2018.
@proceedings{DBLP:conf/itasec/2018,
title = {Proceedings of the Second Italian Conference on Cyber Security, Milan,
Italy, February 6th - to - 9th, 2018},
editor = {Elena Ferrari and Marco Baldi and Roberto Baldoni},
url = {http://ceur-ws.org/Vol-2058},
year = {2018},
date = {2018-01-01},
volume = {2058},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
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Ferrari, Elena
Access Control Book Section
In: Liu, Ling; Ö, Tamer M (Ed.): Encyclopedia of Database Systems, Second Edition, Springer, 2018.
@incollection{DBLP:reference/db/Ferrari18,
title = {Access Control},
author = {Elena Ferrari},
editor = {Ling Liu and Tamer M \"{O}},
url = {https://doi.org/10.1007/978-1-4614-8265-9_6},
doi = {10.1007/978-1-4614-8265-9_6},
year = {2018},
date = {2018-01-01},
booktitle = {Encyclopedia of Database Systems, Second Edition},
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Ferrari, Elena
Access Control Administration Policies Book Section
In: Liu, Ling; Ö, Tamer M (Ed.): Encyclopedia of Database Systems, Second Edition, Springer, 2018.
@incollection{DBLP:reference/db/Ferrari18a,
title = {Access Control Administration Policies},
author = {Elena Ferrari},
editor = {Ling Liu and Tamer M \"{O}},
url = {https://doi.org/10.1007/978-1-4614-8265-9_332},
doi = {10.1007/978-1-4614-8265-9_332},
year = {2018},
date = {2018-01-01},
booktitle = {Encyclopedia of Database Systems, Second Edition},
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Ferrari, Elena
Database Security Book Section
In: Liu, Ling; Ö, Tamer M (Ed.): Encyclopedia of Database Systems, Second Edition, Springer, 2018.
@incollection{DBLP:reference/db/Ferrari18b,
title = {Database Security},
author = {Elena Ferrari},
editor = {Ling Liu and Tamer M \"{O}},
url = {https://doi.org/10.1007/978-1-4614-8265-9_111},
doi = {10.1007/978-1-4614-8265-9_111},
year = {2018},
date = {2018-01-01},
booktitle = {Encyclopedia of Database Systems, Second Edition},
publisher = {Springer},
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Akcora, Cuneyt Gurcan; Ferrari, Elena
Graphical User Interfaces for Privacy Settings Book Section
In: Alhajj, Reda; Rokne, Jon G (Ed.): Encyclopedia of Social Network Analysis and Mining, 2nd Edition, Springer, 2018.
@incollection{DBLP:reference/snam/AkcoraF18,
title = {Graphical User Interfaces for Privacy Settings},
author = {Cuneyt Gurcan Akcora and Elena Ferrari},
editor = {Reda Alhajj and Jon G Rokne},
url = {https://doi.org/10.1007/978-1-4939-7131-2_360},
doi = {10.1007/978-1-4939-7131-2_360},
year = {2018},
date = {2018-01-01},
booktitle = {Encyclopedia of Social Network Analysis and Mining, 2nd Edition},
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Akcora, Cuneyt Gurcan; Ferrari, Elena
Similarity Metrics on Social Networks Book Section
In: Alhajj, Reda; Rokne, Jon G (Ed.): Encyclopedia of Social Network Analysis and Mining, 2nd Edition, Springer, 2018.
@incollection{DBLP:reference/snam/AkcoraF18a,
title = {Similarity Metrics on Social Networks},
author = {Cuneyt Gurcan Akcora and Elena Ferrari},
editor = {Reda Alhajj and Jon G Rokne},
url = {https://doi.org/10.1007/978-1-4939-7131-2_252},
doi = {10.1007/978-1-4939-7131-2_252},
year = {2018},
date = {2018-01-01},
booktitle = {Encyclopedia of Social Network Analysis and Mining, 2nd Edition},
publisher = {Springer},
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Sagirlar, Gokhan; Carminati, Barbara; Ferrari, Elena
Decentralizing Privacy Enforcement for Internet of Things Smart Objects Journal Article
In: CoRR, vol. abs/1804.02161, 2018.
@article{DBLP:journals/corr/abs-1804-02161,
title = {Decentralizing Privacy Enforcement for Internet of Things Smart Objects},
author = {Gokhan Sagirlar and Barbara Carminati and Elena Ferrari},
url = {http://arxiv.org/abs/1804.02161},
year = {2018},
date = {2018-01-01},
journal = {CoRR},
volume = {abs/1804.02161},
abstract = {Internet of Things (IoT) is now evolving into a loosely coupled, decentralized system of cooperating smart objects, where high-speed data processing, analytics and shorter response times are becoming more necessary than ever. Such decentralization has a great impact on the way personal information generated and consumed by smart objects should be protected, because, without centralized data management, it is more difficult to control how data are combined and used by smart objects. To cope with this issue, in this paper, we propose a framework where users of smart objects can specify their privacy preferences. Compliance check of user individual privacy preferences is performed directly by smart objects. Moreover, acknowledging that embedding the enforcement mechanism into smart objects implies some overhead, we have extensively tested the proposed framework on different scenarios, and the obtained results show the feasibility of our approach.},
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Internet of Things (IoT) is now evolving into a loosely coupled, decentralized system of cooperating smart objects, where high-speed data processing, analytics and shorter response times are becoming more necessary than ever. Such decentralization has a great impact on the way personal information generated and consumed by smart objects should be protected, because, without centralized data management, it is more difficult to control how data are combined and used by smart objects. To cope with this issue, in this paper, we propose a framework where users of smart objects can specify their privacy preferences. Compliance check of user individual privacy preferences is performed directly by smart objects. Moreover, acknowledging that embedding the enforcement mechanism into smart objects implies some overhead, we have extensively tested the proposed framework on different scenarios, and the obtained results show the feasibility of our approach.
Sagirlar, Gokhan; Carminati, Barbara; Ferrari, Elena; Sheehan, John D; Ragnoli, Emanuele
Hybrid-IoT: Hybrid Blockchain Architecture for Internet of Things - PoW Sub-blockchains Journal Article
In: CoRR, vol. abs/1804.03903, 2018.
@article{DBLP:journals/corr/abs-1804-03903,
title = {Hybrid-IoT: Hybrid Blockchain Architecture for Internet of Things
- PoW Sub-blockchains},
author = {Gokhan Sagirlar and Barbara Carminati and Elena Ferrari and John D Sheehan and Emanuele Ragnoli},
url = {http://arxiv.org/abs/1804.03903},
year = {2018},
date = {2018-01-01},
journal = {CoRR},
volume = {abs/1804.03903},
abstract = {From its early days the Internet of Things (IoT) has evolved into a decentralized system of cooperating smart objects with the requirement, among others, of achieving distributed consensus. Yet, current IoT platform solutions are centralized cloud based computing infrastructures, manifesting a number of significant disadvantages, such as, among others, high cloud server maintenance costs, weakness for supporting time-critical IoT applications, security and trust issues. Enabling blockchain technology into IoT can help to achieve a proper distributed consensus based IoT system that overcomes those disadvantages. While this is an ideal match, it is still a challenging endeavor. In this paper we take a first step towards that goal by designing Hybrid-IoT, a hybrid blockchain architecture for IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Then, the connection among the PoW subblockchains employs a BFT inter-connector framework, such as Polkadot or Cosmos. In this paper, we focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds. In order to prove the validity of the approach we carry on a performance and security evaluation.},
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From its early days the Internet of Things (IoT) has evolved into a decentralized system of cooperating smart objects with the requirement, among others, of achieving distributed consensus. Yet, current IoT platform solutions are centralized cloud based computing infrastructures, manifesting a number of significant disadvantages, such as, among others, high cloud server maintenance costs, weakness for supporting time-critical IoT applications, security and trust issues. Enabling blockchain technology into IoT can help to achieve a proper distributed consensus based IoT system that overcomes those disadvantages. While this is an ideal match, it is still a challenging endeavor. In this paper we take a first step towards that goal by designing Hybrid-IoT, a hybrid blockchain architecture for IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Then, the connection among the PoW subblockchains employs a BFT inter-connector framework, such as Polkadot or Cosmos. In this paper, we focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds. In order to prove the validity of the approach we carry on a performance and security evaluation.
Sagirlar, Gokhan; Carminati, Barbara; Ferrari, Elena
AutoBotCatcher: Blockchain-based P2P Botnet Detection for the Internet of Things Journal Article
In: CoRR, vol. abs/1809.10775, 2018.
@article{DBLP:journals/corr/abs-1809-10775,
title = {AutoBotCatcher: Blockchain-based P2P Botnet Detection for the Internet
of Things},
author = {Gokhan Sagirlar and Barbara Carminati and Elena Ferrari},
url = {http://arxiv.org/abs/1809.10775},
year = {2018},
date = {2018-01-01},
journal = {CoRR},
volume = {abs/1809.10775},
abstract = {In general, a botnet is a collection of compromised internet computers, controlled by attackers for malicious purposes. To increase attacks' success chance and resilience against defence mechanisms, modern botnets have often a decentralized P2P structure. Here, IoT devices are playing a critical role, becoming one of the major tools for malicious parties to perform attacks. Notable examples are DDoS attacks on Krebs on Security and DYN, which have been performed by IoT devices part of botnets. We take a first step towards detecting P2P botnets in IoT, by proposing AutoBotCatcher, whose design is driven by the consideration that bots of the same botnet frequently communicate with each other and form communities. As such, the purpose of AutoBotCatcher is to dynamically analyze communities of IoT devices, formed according to their network traffic flows, to detect botnets. AutoBotCatcher exploits a Byzantine Fault Tolerant (BFT) blockchain, as a state transition machine that allows collaboration of multiple parties without trust, in order to perform collaborative and dynamic botnet detection by collecting and auditing IoT devices' network traffic flows as blockchain transactions. In this paper, we focus on the design of the AutoBotCatcher by first defining the blockchain structure underlying AutoBot-Catcher, then discussing its components.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In general, a botnet is a collection of compromised internet computers, controlled by attackers for malicious purposes. To increase attacks' success chance and resilience against defence mechanisms, modern botnets have often a decentralized P2P structure. Here, IoT devices are playing a critical role, becoming one of the major tools for malicious parties to perform attacks. Notable examples are DDoS attacks on Krebs on Security and DYN, which have been performed by IoT devices part of botnets. We take a first step towards detecting P2P botnets in IoT, by proposing AutoBotCatcher, whose design is driven by the consideration that bots of the same botnet frequently communicate with each other and form communities. As such, the purpose of AutoBotCatcher is to dynamically analyze communities of IoT devices, formed according to their network traffic flows, to detect botnets. AutoBotCatcher exploits a Byzantine Fault Tolerant (BFT) blockchain, as a state transition machine that allows collaboration of multiple parties without trust, in order to perform collaborative and dynamic botnet detection by collecting and auditing IoT devices' network traffic flows as blockchain transactions. In this paper, we focus on the design of the AutoBotCatcher by first defining the blockchain structure underlying AutoBot-Catcher, then discussing its components.
2017
Colombo, Pietro; Ferrari, Elena
Enhancing NoSQL datastores with fine-grained context-aware access control: a preliminary study on MongoDB Journal Article
In: Int. J. Cloud Comput., vol. 6, no. 4, pp. 292–305, 2017.
@article{DBLP:journals/ijcc/ColomboF17,
title = {Enhancing NoSQL datastores with fine-grained context-aware access
control: a preliminary study on MongoDB},
author = {Pietro Colombo and Elena Ferrari},
url = {https://doi.org/10.1504/IJCC.2017.10011284},
doi = {10.1504/IJCC.2017.10011284},
year = {2017},
date = {2017-01-01},
journal = {Int. J. Cloud Comput.},
volume = {6},
number = {4},
pages = {292--305},
abstract = {NoSQL datastores are getting increasing attention by companies and organisation for the ease and efficiency of handling high volumes of heterogeneous and unstructured data. Nowadays, as majority of these systems are available as cloud based services, this potentially favours their use even among small companies that could not afford the management of server farms for local cluster based solutions. However, besides all their benefits in terms of performance, availability and scalability as well as support for advanced analysis forms, NoSQL datastores also have some weaknesses, such as poor natively provided support for data protection. Recent surveys show that several companies consider the poor support for security features of NoSQL databases as a valid reason not to use them (Intel Co., 2013). In this paper, we do a first step to overcome these weaknesses by first proposing a roadmap to enhance the data protection functionalities of NoSQL datastores. Then, we illustrate our preliminary experience of designing an enhanced access control mechanism for MongoDB (http://www.mongodb.org), which, according to recent surveys (DB-Engines Ranking, 2017) ranks as the most popular NoSQL database.},
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NoSQL datastores are getting increasing attention by companies and organisation for the ease and efficiency of handling high volumes of heterogeneous and unstructured data. Nowadays, as majority of these systems are available as cloud based services, this potentially favours their use even among small companies that could not afford the management of server farms for local cluster based solutions. However, besides all their benefits in terms of performance, availability and scalability as well as support for advanced analysis forms, NoSQL datastores also have some weaknesses, such as poor natively provided support for data protection. Recent surveys show that several companies consider the poor support for security features of NoSQL databases as a valid reason not to use them (Intel Co., 2013). In this paper, we do a first step to overcome these weaknesses by first proposing a roadmap to enhance the data protection functionalities of NoSQL datastores. Then, we illustrate our preliminary experience of designing an enhanced access control mechanism for MongoDB (http://www.mongodb.org), which, according to recent surveys (DB-Engines Ranking, 2017) ranks as the most popular NoSQL database.
Albertini, Davide Alberto; Carminati, Barbara; Ferrari, Elena
An extended access control mechanism exploiting data dependencies Journal Article
In: Int. J. Inf. Sec., vol. 16, no. 1, pp. 75–89, 2017.
@article{DBLP:journals/ijisec/AlbertiniCF17,
title = {An extended access control mechanism exploiting data dependencies},
author = {Davide Alberto Albertini and Barbara Carminati and Elena Ferrari},
url = {https://doi.org/10.1007/s10207-016-0322-4},
doi = {10.1007/s10207-016-0322-4},
year = {2017},
date = {2017-01-01},
journal = {Int. J. Inf. Sec.},
volume = {16},
number = {1},
pages = {75--89},
abstract = {In general, access control mechanisms in DBMSs ensure that users access only those portions of data for which they have authorizations, according to a predefined set of access control policies. However, it has been shown that access control mechanisms might be not enough. A clear example is the inference problem due to functional dependencies, which might allow a user to discover unauthorized data by exploiting authorized data. In this paper, we wish to investigate data dependencies (e.g., functional dependencies, foreign key constraints, and knowledge-based implications) from a different perspective. In particular, the aim was to investigate data dependencies as a mean for increasing the DBMS utility, that is, the number of queries that can be safely answered, rather than as channels for releasing sensitive data. We believe that, under given circumstances, this unauthorized release may give more benefits than issues. As such, we present a query rewriting technique capable of extending defined access control policies by exploiting data dependencies, in order to authorize unauthorized but inferable data.In general, access control mechanisms in DBMSs ensure that users access only those portions of data for which they have authorizations, according to a predefined set of access control policies. However, it has been shown that access control mechanisms might be not enough. A clear example is the inference problem due to functional dependencies, which might allow a user to discover unauthorized data by exploiting authorized data. In this paper, we wish to investigate data dependencies (e.g., functional dependencies, foreign key constraints, and knowledge-based implications) from a different perspective. In particular, the aim was to investigate data dependencies as a mean for increasing the DBMS utility, that is, the number of queries that can be safely answered, rather than as channels for releasing sensitive data. We believe that, under given circumstances, this unauthorized release may give more benefits than issues. As such, we present a query rewriting technique capable of extending defined access control policies by exploiting data dependencies, in order to authorize unauthorized but inferable data.},
keywords = {},
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In general, access control mechanisms in DBMSs ensure that users access only those portions of data for which they have authorizations, according to a predefined set of access control policies. However, it has been shown that access control mechanisms might be not enough. A clear example is the inference problem due to functional dependencies, which might allow a user to discover unauthorized data by exploiting authorized data. In this paper, we wish to investigate data dependencies (e.g., functional dependencies, foreign key constraints, and knowledge-based implications) from a different perspective. In particular, the aim was to investigate data dependencies as a mean for increasing the DBMS utility, that is, the number of queries that can be safely answered, rather than as channels for releasing sensitive data. We believe that, under given circumstances, this unauthorized release may give more benefits than issues. As such, we present a query rewriting technique capable of extending defined access control policies by exploiting data dependencies, in order to authorize unauthorized but inferable data.In general, access control mechanisms in DBMSs ensure that users access only those portions of data for which they have authorizations, according to a predefined set of access control policies. However, it has been shown that access control mechanisms might be not enough. A clear example is the inference problem due to functional dependencies, which might allow a user to discover unauthorized data by exploiting authorized data. In this paper, we wish to investigate data dependencies (e.g., functional dependencies, foreign key constraints, and knowledge-based implications) from a different perspective. In particular, the aim was to investigate data dependencies as a mean for increasing the DBMS utility, that is, the number of queries that can be safely answered, rather than as channels for releasing sensitive data. We believe that, under given circumstances, this unauthorized release may give more benefits than issues. As such, we present a query rewriting technique capable of extending defined access control policies by exploiting data dependencies, in order to authorize unauthorized but inferable data.
Colombo, Pietro; Ferrari, Elena
Enhancing MongoDB with Purpose-Based Access Control Journal Article
In: IEEE Trans. Dependable Secur. Comput., vol. 14, no. 6, pp. 591–604, 2017.
@article{DBLP:journals/tdsc/ColomboF17,
title = {Enhancing MongoDB with Purpose-Based Access Control},
author = {Pietro Colombo and Elena Ferrari},
url = {https://doi.org/10.1109/TDSC.2015.2497680},
doi = {10.1109/TDSC.2015.2497680},
year = {2017},
date = {2017-01-01},
journal = {IEEE Trans. Dependable Secur. Comput.},
volume = {14},
number = {6},
pages = {591--604},
abstract = {Privacy has become a key requirement for data management systems. Nevertheless, NoSQL datastores, namely highly scalable non relational database management systems, which often support data management of Internet scale applications,still do not provide support for privacy policies enforcement. With this work, we begin to address this issue, by proposing an approach for the integration of purpose based policy enforcement capabilities into MongoDB, a popular NoSQL datastore. Our contribution consists of the enhancement of the MongoDB role based access control model with privacy concepts and related enforcement monitor. The proposed monitor is easily integrable into any MongoDB deployment through simple configurations. Experimental results show that our monitor enforces purpose-based access control with low overhead.},
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Privacy has become a key requirement for data management systems. Nevertheless, NoSQL datastores, namely highly scalable non relational database management systems, which often support data management of Internet scale applications,still do not provide support for privacy policies enforcement. With this work, we begin to address this issue, by proposing an approach for the integration of purpose based policy enforcement capabilities into MongoDB, a popular NoSQL datastore. Our contribution consists of the enhancement of the MongoDB role based access control model with privacy concepts and related enforcement monitor. The proposed monitor is easily integrable into any MongoDB deployment through simple configurations. Experimental results show that our monitor enforces purpose-based access control with low overhead.
Ilia, Panagiotis; Carminati, Barbara; Ferrari, Elena; Fragopoulou, Paraskevi; Ioannidis, Sotiris
SAMPAC: Socially-Aware collaborative Multi-Party Access Control Proceedings Article
In: -, Gail; Pretschner, Alexander; Ghinita, Gabriel (Ed.): Proceedings of the Seventh ACM Conference on Data and Application Security and Privacy, CODASPY 2017, Scottsdale, AZ, USA, March 22-24, 2017, pp. 71–82, ACM, 2017.
@inproceedings{DBLP:conf/codaspy/IliaCFFI17,
title = {SAMPAC: Socially-Aware collaborative Multi-Party Access Control},
author = {Panagiotis Ilia and Barbara Carminati and Elena Ferrari and Paraskevi Fragopoulou and Sotiris Ioannidis},
editor = {Gail - and Alexander Pretschner and Gabriel Ghinita},
url = {https://doi.org/10.1145/3029806.3029834},
doi = {10.1145/3029806.3029834},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the Seventh ACM Conference on Data and Application
Security and Privacy, CODASPY 2017, Scottsdale, AZ, USA, March 22-24,
2017},
pages = {71--82},
publisher = {ACM},
abstract = {According to the current design of content sharing services, such as Online Social Networks (OSNs), typically (i) the service provider has unrestricted access to the uploaded resources and (ii) only the user uploading the resource is allowed to define access control permissions over it. This results in a lack of control from other users that are associated, in some way, with that resource. To cope with these issues, in this paper, we propose a privacy-preserving system that allows users to upload their resources encrypted, and we design a collaborative multi-party access control model allowing all the users related to a resource to participate in the specification of the access control policy. Our model employs a threshold-based secret sharing scheme, and by exploiting users' social relationships, sets the trusted friends of the associated users responsible to partially enforce the collective policy. Through replication of the secret shares and delegation of the access control enforcement role, our model ensures that resources are timely available when requested. Finally, our experiments demonstrate that the performance overhead of our model is minimal and that it does not significantly affect user experience.According to the current design of content sharing services, such as Online Social Networks (OSNs), typically (i) the service provider has unrestricted access to the uploaded resources and (ii) only the user uploading the resource is allowed to define access control permissions over it. This results in a lack of control from other users that are associated, in some way, with that resource. To cope with these issues, in this paper, we propose a privacy-preserving system that allows users to upload their resources encrypted, and we design a collaborative multi-party access control model allowing all the users related to a resource to participate in the specification of the access control policy. Our model employs a threshold-based secret sharing scheme, and by exploiting users' social relationships, sets the trusted friends of the associated users responsible to partially enforce the collective policy. Through replication of the secret shares and delegation of the access control enforcement role, our model ensures that resources are timely available when requested. Finally, our experiments demonstrate that the performance overhead of our model is minimal and that it does not significantly affect user experience.},
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tppubtype = {inproceedings}
}
According to the current design of content sharing services, such as Online Social Networks (OSNs), typically (i) the service provider has unrestricted access to the uploaded resources and (ii) only the user uploading the resource is allowed to define access control permissions over it. This results in a lack of control from other users that are associated, in some way, with that resource. To cope with these issues, in this paper, we propose a privacy-preserving system that allows users to upload their resources encrypted, and we design a collaborative multi-party access control model allowing all the users related to a resource to participate in the specification of the access control policy. Our model employs a threshold-based secret sharing scheme, and by exploiting users' social relationships, sets the trusted friends of the associated users responsible to partially enforce the collective policy. Through replication of the secret shares and delegation of the access control enforcement role, our model ensures that resources are timely available when requested. Finally, our experiments demonstrate that the performance overhead of our model is minimal and that it does not significantly affect user experience.According to the current design of content sharing services, such as Online Social Networks (OSNs), typically (i) the service provider has unrestricted access to the uploaded resources and (ii) only the user uploading the resource is allowed to define access control permissions over it. This results in a lack of control from other users that are associated, in some way, with that resource. To cope with these issues, in this paper, we propose a privacy-preserving system that allows users to upload their resources encrypted, and we design a collaborative multi-party access control model allowing all the users related to a resource to participate in the specification of the access control policy. Our model employs a threshold-based secret sharing scheme, and by exploiting users' social relationships, sets the trusted friends of the associated users responsible to partially enforce the collective policy. Through replication of the secret shares and delegation of the access control enforcement role, our model ensures that resources are timely available when requested. Finally, our experiments demonstrate that the performance overhead of our model is minimal and that it does not significantly affect user experience.
Singh, Bikash Chandra; Carminati, Barbara; Ferrari, Elena
Learning Privacy Habits of PDS Owners Proceedings Article
In: Lee, Kisung; Liu, Ling (Ed.): 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017, Atlanta, GA, USA, June 5-8, 2017, pp. 151–161, IEEE Computer Society, 2017.
@inproceedings{DBLP:conf/icdcs/SinghCF17,
title = {Learning Privacy Habits of PDS Owners},
author = {Bikash Chandra Singh and Barbara Carminati and Elena Ferrari},
editor = {Kisung Lee and Ling Liu},
url = {https://doi.org/10.1109/ICDCS.2017.65},
doi = {10.1109/ICDCS.2017.65},
year = {2017},
date = {2017-01-01},
booktitle = {37th IEEE International Conference on Distributed Computing Systems,
ICDCS 2017, Atlanta, GA, USA, June 5-8, 2017},
pages = {151--161},
publisher = {IEEE Computer Society},
abstract = {The concept of Personal Data Storage (PDS) has recently emerged as an alternative and innovative way of managing personal data w.r.t. the service-centric one commonly used today. The PDS offers a unique logical repository, allowing individuals to collect, store, and give access to their data to third parties. The research on PDS has so far mainly focused on the enforcement mechanisms, that is, on how user privacy preferences can be enforced. In contrast, the fundamental issue of preference specification has been so far not deeply investigated. In this paper, we do a step in this direction by proposing different learning algorithms that allow a fine-grained learning of the privacy aptitudes of PDS owners. The learned models are then used to answer third party access requests. The extensive experiments we have performed show the effectiveness of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The concept of Personal Data Storage (PDS) has recently emerged as an alternative and innovative way of managing personal data w.r.t. the service-centric one commonly used today. The PDS offers a unique logical repository, allowing individuals to collect, store, and give access to their data to third parties. The research on PDS has so far mainly focused on the enforcement mechanisms, that is, on how user privacy preferences can be enforced. In contrast, the fundamental issue of preference specification has been so far not deeply investigated. In this paper, we do a step in this direction by proposing different learning algorithms that allow a fine-grained learning of the privacy aptitudes of PDS owners. The learned models are then used to answer third party access requests. The extensive experiments we have performed show the effectiveness of the proposed approach.