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Research Spirit – Rethinking access control and security services for a better controlled, better protected information.


Research Spirit – Scrutinizing thoughts to design non-traditional solutions for a better understood, better managed, and better owned information privacy for all.

Trust & Risk

Research Spirit – Designing risk management, trust measures, and social reputation models for a safer e-social world.


PAutoBotCatcher: A blockchain-based privacy-preserving botnet detector for Internet of Things

STRICT SociaLab members Prof. Elena Ferrari, Prof. Barbara Carminati, and Ahmed Lekssays have published their new paper entitled: “PAutoBotCatcher: A blockchain-based privacy-preserving botnet detector for Internet of Things” at Computer Networks journal.

The following is the abstract of the new publication:

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.

University of Insubria organized the Security, Privacy and Trust for Wearable Devices Workshop

STRICT SociaLab organized the “Security, Privacy and Trust for Wearable Devices” workshop in the framework of RAIS project. The workshop was held in Heraklion, Greece on September 9-10, 2021 as a part of RAIS activities that involved a summer school and an entrepreneurship workshop.

The following is the program with the speakers and their presentations’ titles:

SpeakerPresentation Title
Elisa BertinoSecurity of 4G and 5G cellular networks
Hamed HaddadiSafeguarding against Information Exposure From Consumer IoT Devices
Michael SirivianosCharacterizing abhorrent misinformative and mistargeted content on YouTube
Mauro ContiSide and Covert Channels: the Dr. Jekyll and Mr Hyde of Modern Technologies
Shahid RazaAutomated cybersecurity for Internet-connected Things
Arthur van der WeesSense & Sensibility in Sports: Personal & Interdependent Wearables that Work