Title: DROPWAT: an Invisible Network Flow Watermark for Data Exfiltration Traceback,
Abstract: Watermarking techniques have been proposed during the last 10 years as an
approach to trace network flows for intrusion detection purposes. These
techniques aim to impress a hidden signature on a traffic flow. A central
property of network flow watermarking is invisibility, i.e., the ability to go
unidentified by an unauthorized third party. Although widely sought after, the
development of an invisible watermark is a challenging task that has not yet
been accomplished.
In this paper we take a step forward in addressing the invisibility problem
with DROPWAT, an active network flow watermarking technique developed for
tracing Internet flows directed to the staging server that is the final
destination in a data exfiltration attack, even in the presence of several
intermediate stepping stones or an anonymous network. DROPWAT is a timing-based
technique that indirectly modifies interpacket delays by exploiting network
reaction to packet loss. We empirically demonstrate that the watermark embedded
by means of DROPWAT is invisible to a third party observing the watermarked
traffic. We also validate DROPWAT and analyze its performance in a controlled
experimental framework involving the execution of a series of experiments on
the Internet, using Web proxy servers as stepping stones executed on several
instances in Amazon Web Services, as well as the TOR anonymous network in the
place of the stepping stones. Our results show that the detection algorithm is
able to identify an embedded watermark achieving over 95% accuracy while being
invisible.