Traffic Morphing: An Efficient Defense Against Statistical Traffic Analysis

By Charles V. Wright, Scott E. Coull, Fabian Monrose. Network and Distributed System Security Symposium, 2009

370 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/54bba8ef46ed5e8ac125cea0bf2f78a5b226a927

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Citations in the corpus

On the Security of Machine Learning in Malware C&C Detection (2016, background): “Wright et al. [2009] show that it can be enough to simply emulate the traffic feature(s) that is the focus of the detection system.”
Website Fingerprinting at Internet Scale (2016, background): “While padding operates on a per-packet level, traffic morphing aims to adapt a complete packet trace such that it looks similar to another packet trace [31].”
Cyber security in the Smart Grid: Survey and challenges (2013, background): “Typical examples include wiretappers [60] and traffic analyzers [61].”
Effective Attacks and Provable Defenses for Website Fingerprinting (2014, background): “Simulatable, random: This includes traffic morphing [24], which allows a client to load a web page using a packet size distribution from a different page, and Panchenko’s background noise [17], where a decoy page is loaded simultaneously with the real page to hide the real packet sequence.”
A Systematic Approach to Developing and Evaluating Website Fingerprinting Defenses (2014, methodology): “Pa-Decoy fails to completely cover interpacket timing because it only covers the total transmission time roughly half the time (i.e., when the decoy page takes longer to load than the desired page), which may leak the total page size, a powerful feature.”
Website fingerprinting in onion routing based anonymization networks (2011, background): “[24] analyzed the influence of morphing as a countermeasure against statistical traffic analysis.”
Improved website fingerprinting on Tor (2013, methodology): “In 2005, Liberatore and Levine [8] published two methods for “inferring the source of encrypted HTTP connections”.”