Monitoring Anonymous P2P File-Sharing Systems

2 PagesInternational audienceAnonymous communications have been exponentially growing, where more and more users are shifting to a privacy-preserving Internet and anonymising their peer-to-peer communications. Anonymous systems allow users to access different services while preserving their anonymity. We aim to characterise these anonymous systems, with a special focus in the I2P network. Current statistics service for the I2P network do not provide values about the type of applications deployed in the network nor the geographical localisation of users. Our objective is to determine the number of users in the network, the number of anonymous applications, and the type of those applications. We also explore the possibility of inferring which group of users is responsible for the activity of an anonymous application. Thus, we improve the current I2P statistics and get better insights of the network

Adaptive Traffic Fingerprinting for Darknet Threat Intelligence

Darknet technology such as Tor has been used by various threat actors for organising illegal activities and data exfiltration. As such, there is a case for organisations to block such traffic, or to try and identify when it is used and for what purposes. However, anonymity in cyberspace has always been a domain of conflicting interests. While it gives enough power to nefarious actors to masquerade their illegal activities, it is also the cornerstone to facilitate freedom of speech and privacy. We present a proof of concept for a novel algorithm that could form the fundamental pillar of a darknet-capable Cyber Threat Intelligence platform. The solution can reduce anonymity of users of Tor, and considers the existing visibility of network traffic before optionally initiating targeted or widespread BGP interception. In combination with server HTTP response manipulation, the algorithm attempts to reduce the candidate data set to eliminate client-side traffic that is most unlikely to be responsible for server-side connections of interest. Our test results show that MITM manipulated server responses lead to expected changes received by the Tor client. Using simulation data generated by shadow, we show that the detection scheme is effective with false positive rate of 0.001, while sensitivity detecting non-targets was 0.016+-0.127. Our algorithm could assist collaborating organisations willing to share their threat intelligence or cooperate during investigations.Comment: 26 page