Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-15497-3_13
Title: Website fingerprinting and identification using ordered feature sequences
Authors: Lu, L.
Chang, E.-C. 
Chan, M.C. 
Keywords: anonymity
edit distance
privacy
side channel attack
Traffic analysis
Issue Date: 2010
Source: Lu, L.,Chang, E.-C.,Chan, M.C. (2010). Website fingerprinting and identification using ordered feature sequences. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6345 LNCS : 199-214. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-15497-3_13
Abstract: We consider website fingerprinting over encrypted and proxied channel. It has been shown that information on packet sizes is sufficient to achieve good identification accuracy. Recently, traffic morphing [1] was proposed to thwart website fingerprinting by changing the packet size distribution so as to mimic some other website, while minimizing bandwidth overhead. In this paper, we point out that packet ordering information, though noisy, can be utilized to enhance website fingerprinting. In addition, traces of the ordering information remain even under traffic morphing and they can be extracted for identification. When web access is performed over OpenSSH and 2000 profiled websites, the identification accuracy of our scheme reaches 81%, which is 11% better than Liberatore and Levine's scheme presented in CCS'06 [2]. We are able to identify 78% of the morphed traffic among 2000 websites while Liberatore and Levine's scheme identifies only 52%. Our analysis suggests that an effective countermeasure to website fingerprinting should not only hide the packet size distribution, but also aggressively remove the ordering information. © 2010 Springer-Verlag Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/42089
ISBN: 3642154964
ISSN: 03029743
DOI: 10.1007/978-3-642-15497-3_13
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