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Tracking Users on the Internet with Behavioral Patterns: Evaluation of Its Practical Feasibility

Abstract : Traditionally, service providers, who want to track the activities of Internet users, rely on explicit tracking techniques like HTTP cookies. From a privacy perspective behavior-based tracking is even more dangerous, because it allows service providers to track users passively, i. e., without cookies. In this case multiple sessions of a user are linked by exploiting characteristic patterns mined from network traffic.In this paper we study the feasibility of behavior-based tracking in a real-world setting, which is unknown so far. In principle, behavior-based tracking can be carried out by any attacker that can observe the activities of users on the Internet. We design and implement a behavior-based tracking technique that consists of a Naive Bayes classifier supported by a cosine similarity decision engine. We evaluate our technique using a large-scale dataset that contains all queries received by a DNS resolver that is used by more than 2100 concurrent users on average per day. Our technique is able to correctly link 88.2 % of the surfing sessions on a day-to-day basis. We also discuss various countermeasures that reduce the effectiveness of our technique.
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Christian Banse, Dominik Herrmann, Hannes Federrath. Tracking Users on the Internet with Behavioral Patterns: Evaluation of Its Practical Feasibility. 27th Information Security and Privacy Conference (SEC), Jun 2012, Heraklion, Crete, Greece. pp.235-248, ⟨10.1007/978-3-642-30436-1_20⟩. ⟨hal-01518230⟩



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