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.