DROPWAT: an Invisible Network Flow Watermark for Data Exfiltration Traceback

TL;DR

DROPWAT is a novel timing-based network flow watermarking technique that invisibly traces data exfiltration flows across complex networks, including anonymous and multi-hop paths, with high detection accuracy.

Contribution

It introduces DROPWAT, the first active watermarking method that remains invisible to third parties while effectively tracing exfiltration traffic through various network conditions.

Findings

Watermark is invisible to third-party observers.

Detection accuracy exceeds 95% in experiments.

Effective across different network scenarios including TOR.

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.

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