Adaptive Traffic Fingerprinting for Darknet Threat Intelligence

TL;DR

This paper introduces a novel algorithm for reducing anonymity in darknet traffic, specifically Tor, to enhance threat intelligence capabilities by identifying potential malicious users through traffic analysis and BGP interception techniques.

Contribution

The paper presents a new algorithm that combines traffic fingerprinting with BGP interception and server response manipulation to improve darknet user identification.

Findings

Effective detection with false positive rate of 0.001

Sensitivity to non-targets at 0.016+-0.127

Test results demonstrate potential for threat intelligence applications

Abstract

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.