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Touching from a Distance

This article delves into web page fingerprinting attacks, exploring their mechanisms and defenses. It identifies how packet ordering can reveal information about web pages, discusses ad hoc defenses, and highlights techniques like HTTPOS and randomized pipelining over Tor. The piece explains how attackers can classify packet traces and infer user online activities using models like Hidden Markov Models. The conclusions emphasize the vulnerabilities that exist even with prevalent defenses, and the costs of relying solely on traffic morphing and other strategies against scrutiny.

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Touching from a Distance

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  1. Touching from a Distance Website Fingerprinting Attacks and Defenses

  2. In a nutshell … • Web page fingerprinting attack • Dodges defences such as • HTTPOS • Randomized pipelining over Tor • Ad hoc defenses unsuccessful

  3. RECOGNIZING WEB PAGES • Identify web pages with the help of Packet ordering • Order of incoming and outgoing packets reveals information about • The size of objects referenced in a page • The order in which the browser re- quests them.

  4. RECOGNIZING WEB PAGES • Computes the similarity of packet traces generated when a browser loads a web page • Converts traces into strings • Uses a distance metric to compare them • Trains a supportvector machine using a kernelbased on editdistance

  5. RECOGNIZING WEB SITES Hidden Markov Model • capture the link structure of the site • capture the probable paths that users will follow among the pages when visiting the site. • use page fingerprinting technique to classify the packet traces

  6. RECOGNIZING WEB SITES • Forward algorithm • Probability of observed trace of packets being generated when loading pages from the target web site • Attacker can know if a sequence of a victim’s page loads are all from the same web site

  7. CONCLUSIONS • Local or national governments can snoop on citizens • Traffic morphing, HTTPOSand randomized pipeliningimpose high costs • Do not offer the guarantee of being impregnable to attacks • Proposed web site classifier is able to infer user’s online activities.

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