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<article xlink="http://www.w3.org/1999/xlink" dtd-version="1.0" article-type="general-sciences" lang="en"><front><journal-meta><journal-id journal-id-type="publisher">IJCRR</journal-id><journal-id journal-id-type="nlm-ta">I Journ Cur Res Re</journal-id><journal-title-group><journal-title>International Journal of Current Research and Review</journal-title><abbrev-journal-title abbrev-type="pubmed">I Journ Cur Res Re</abbrev-journal-title></journal-title-group><issn pub-type="ppub">2231-2196</issn><issn pub-type="opub">0975-5241</issn><publisher><publisher-name>Radiance Research Academy</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">1697</article-id><article-id pub-id-type="doi"/><article-id pub-id-type="doi-url"/><article-categories><subj-group subj-group-type="heading"><subject>General Sciences</subject></subj-group></article-categories><title-group><article-title>TRAFFIC ANALYSIS ATTACKS ON ANONYMITY NETWORKS&#13;
</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>R</surname><given-names>Vetrivendan.</given-names></name></contrib></contrib-group><pub-date pub-type="ppub"><day>28</day><month>08</month><year>2012</year></pub-date><volume>)</volume><issue/><fpage>120</fpage><lpage>124</lpage><permissions><copyright-statement>This article is copyright of Popeye Publishing, 2009</copyright-statement><copyright-year>2009</copyright-year><license license-type="open-access" href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.</license-p></license></permissions><abstract><p>In this paper, we focus on a particular class of traffic analysis attacks, flow correlation attacks, by which an adversary attempts to analyze the network traffic and correlate the traffic of a flow over an input link with that over an output link. Two classes of correlation methods are considered, namely time-domain methods and frequency-domain methods. Based on our threat model and known strategies in existing mix networks, we perform extensive experiments to analyze the performance of mixes. We find that all but a few batching strategies fail against flow-correlation attacks, allowing the adversary to either identify ingress or egress points of a flow or to reconstruct the path used by the flow. Counter intuitively, some batching strategies are actually detrimental against attacks.&#13;
</p></abstract><kwd-group><kwd>Traffic analysis</kwd><kwd> flow-correlation attack</kwd><kwd> counter intuitively</kwd><kwd> detrimental attacks.</kwd></kwd-group></article-meta></front></article>
