29,271 research outputs found
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
RAPTOR: Routing Attacks on Privacy in Tor
The Tor network is a widely used system for anonymous communication. However,
Tor is known to be vulnerable to attackers who can observe traffic at both ends
of the communication path. In this paper, we show that prior attacks are just
the tip of the iceberg. We present a suite of new attacks, called Raptor, that
can be launched by Autonomous Systems (ASes) to compromise user anonymity.
First, AS-level adversaries can exploit the asymmetric nature of Internet
routing to increase the chance of observing at least one direction of user
traffic at both ends of the communication. Second, AS-level adversaries can
exploit natural churn in Internet routing to lie on the BGP paths for more
users over time. Third, strategic adversaries can manipulate Internet routing
via BGP hijacks (to discover the users using specific Tor guard nodes) and
interceptions (to perform traffic analysis). We demonstrate the feasibility of
Raptor attacks by analyzing historical BGP data and Traceroute data as well as
performing real-world attacks on the live Tor network, while ensuring that we
do not harm real users. In addition, we outline the design of two monitoring
frameworks to counter these attacks: BGP monitoring to detect control-plane
attacks, and Traceroute monitoring to detect data-plane anomalies. Overall, our
work motivates the design of anonymity systems that are aware of the dynamics
of Internet routing
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