Analyzing Characteristic Host Access Patterns for Re-identification of Web User Sessions

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• 作者：Dominik Herrmann (1) Dominik.Herrmann@wiwi.uni-r.de
  Christoph Gerber (1) Christoph.Gerber@wiwi.uni-r.de
  Christian Banse (1) Christian.Banse@wiwi.uni-r.de
  Hannes Federrath (1) Hannes.Federrath@wiwi.uni-r.de
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7127
• 期：1
• 页码：136-154
• 全文大小：502.4 KB
• 参考文献：1. Adamic, L., Huberman, B.: Zipf’s Law and the Internet. Glottometrics 3(1), 143–150 (2002)
  2. Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addision Wesley, New York (1999)
  3. Barbaro, M., Zeller, T.: A Face is Exposed for AOL Searcher No. 4417749. The New York Times, August 9 (2006)
  4. Breslau, L., Cue, P., Cao, P., Fan, L., Phillips, G., Shenker, S.: Web Caching and Zipf-like Distributions: Evidence and Implications. In: INFOCOM, pp. 126–134 (1999)
  5. Brickell, J., Shmatikov, V.: The cost of privacy: destruction of data-mining utility in anonymized data publishing. In: KDD 2008: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 70–78. ACM, New York (2008)
  6. Catledge, L.D., Pitkow, J.E.: Characterizing Browsing Behaviors on the World-Wide Web. Georgia Institute of Technology (1995)
  7. Coull, S.E., Collins, M.P., Wright, C.V., Monrose, F., Reiter, M.K.: On Web Browsing Privacy in Anonymized NetFlows. In: Proceedings of the 16th USENIX Security Symposium, Boston, MA (August 2007)
  8. Coull, S.E., Wright, C.V., Keromytisz, A.D., Monrose, F., Reiter, M.K.: Taming the devil: Techniques for evaluating anonymized network data. In: Proceedings of the 15th Network and Distributed Systems Security Symposium (2008)
  9. Coull, S.E., Wright, C.V., Monrose, F., Collins, M.P., Reiter, M.K.: Playing devil’s advocate: Inferring sensitive information from anonymized network traces. In: Proceedings of the Network and Distributed System Security Symposium, pp. 35–47 (2007)
  10. Crovella, M.E., Bestavros, A.: Self-similarity in World Wide Web traffic: evidence and possible causes. IEEE/ACM Trans. Netw. 5(6), 835–846 (1997)
  11. Eckersley, P.: How Unique Is Your Web Browser? Technical report, Electronig Frontier Foundation (2009)
  12. Erman, J., Mahanti, A., Arlitt, M.: Internet Traffic Identification using Machine Learning. In: Proceedings of IEEE Global Telecommunications Conference (GLOBECOM), San Francisco, CA, USA, pp. 1–6 (November 2006)
  13. Herrmann, D., Wendolsky, R., Federrath, H.: Website fingerprinting: attacking popular privacy enhancing technologies with the multinomial na?ve-bayes classifier. In: CCSW 2009: Proceedings of the 2009 ACM Workshop on Cloud Computing Security, pp. 31–42. ACM, New York (2009)
  14. Kellar, M., Watters, C., Shepherd, M.: A field study characterizing Web-based information-seeking tasks. Journal of the American Society for Information Science and Technology 58(7), 999–1018 (2007)
  15. Koukis, D., Antonatos, S., Anagnostakis, K.G.: On the Privacy Risks of Publishing Anonymized IP Network Traces. In: Leitold, H., Markatos, E.P. (eds.) CMS 2006. LNCS, vol. 4237, pp. 22–32. Springer, Heidelberg (2006)
  16. Kumpo?t, M.: Data Preparation for User Profiling from Traffic Log. In: The International Conference on Emerging Security Information, Systems, and Technologies, pp. 89–94 (2007)
  17. Kumpo?t, M.: Context Information and user profiling. PhD thesis, Faculty of Informatics, Masaryk University, Czech Republic (2009)
  18. Kumpo?t, M., Matyá?, V.: User Profiling and Re-identification: Case of University-Wide Network Analysis. In: Fischer-Hübner, S., Lambrinoudakis, C., Pernul, G. (eds.) TrustBus 2009. LNCS, vol. 5695, pp. 1–10. Springer, Heidelberg (2009)
  19. Liberatore, M., Levine, B.N.: Inferring the Source of Encrypted HTTP Connections. In: CCS 2006: Proceedings of the 13th ACM Conference on Computer and Communications Security, pp. 255–263. ACM Press, New York (2006)
  20. Malin, B., Airoldi, E.: The Effects of Location Access Behavior on Re-identification Risk in a Distributed Environment. In: Privacy Enhancing Technologies, pp. 413–429 (2006)
  21. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
  22. Moore, A.W., Zuev, D.: Internet traffic classification using bayesian analysis techniques. In: SIGMETRICS 2005: Proceedings of the 2005 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, pp. 50–60. ACM Press, New York (2005)
  23. Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: IEEE Symposium on Security and Privacy, pp. 111–125 (2008)
  24. Obendorf, H., Weinreich, H., Herder, E., Mayer, M.: Web Page Revisitation Revisited: Implications of a Long-term Click-stream Study of Browser Usage. In: CHI 2007, pp. 597–606. ACM Press (May 2007)
  25. Ohm, P.: Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization. In: Social Science Research Network Working Paper Series (August 2009)
  26. Olivier, M.S.: Distributed Proxies for Browsing Privacy: a Simulation of Flocks. In: SAICSIT ’05: Proceedings of the 2005 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries, pp. 104–112. South African Institute for Computer Scientists and Information Technologists, Republic of South Africa (2005)
  27. Padmanabhan, B., Yang, Y.: Clickprints on the Web: Are there signatures in Web Browsing Data? Working Paper Series (October 2006)
  28. Pang, J., Greenstein, B., Gummadi, R., Seshan, S., Wetherall, D.: 802.11 user fingerprinting. In: MobiCom 2007: Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking, pp. 99–110. ACM, New York (2007)
  29. Pang, R., Allman, M., Paxson, V., Lee, J.: The devil and packet trace anonymization. SIGCOMM Comput. Commun. Rev. 36(1), 29–38 (2006)
  30. Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web usage mining: discovery and applications of usage patterns from Web data. SIGKDD Explor. Newsl. 1(2), 12–23 (2000)
  31. Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty Fuzziness and Knowledge Based Systems 10(5), 557–570 (2002)
  32. Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. SIGCOMM Comput. Commun. Rev. 36(5), 5–16 (2006)
  33. Witten, I.H., Frank, E.: Data Mining. Practical Machine Learning Tools and Techniques. Elsevier, San Francisco (2005)
  34. Wondracek, G., Holz, T., Kirda, E., Kruegel, C.: A Practical Attack to De-Anonymize Social Network Users, iseclab.org
  35. Yang, Y.: Web user behavioral profiling for user identification. Decision Support Systems 49, 261–271 (2010)
  36. Yang, Y.C., Padmanabhan, B.: Toward user patterns for online security: Observation time and online user identification. Decision Support Systems 48, 548–558 (2008)
  37. Zipf, G.K.: The psycho-biology of language. An introduction to dynamic philology, 2nd edn. M.I.T. Press, Cambridge (1968)
  38. Zuev, D., Moore, A.W.: Traffic Classification using a Statistical Approach. In: Dovrolis, C. (ed.) PAM 2005. LNCS, vol. 3431, pp. 321–324. Springer, Heidelberg (2005)
• 作者单位：1. Research Group Security in Distributed Systems, Department of Informatics, University of Hamburg, 22527 Hamburg, Germany
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

An attacker, who is able to observe a web user over a long period of time, learns a lot about his interests. It may be difficult to track users with regularly changing IP addresses, though. We show how patterns mined from web traffic can be used to re-identify a majority of users, i. e. link multiple sessions of them. We implement the web user re-identification attack using a Multinomial Na?ve Bayes classifier and evaluate it using a real-world dataset from 28 users. Our evaluation setup complies with the limited knowledge of an attacker on a malicious web proxy server, who is only able to observe the host names visited by its users. The results suggest that consecutive sessions can be linked with high probability for session durations from 5 minutes to 48 hours and that user profiles degrade only slowly over time. We also propose basic countermeasures and evaluate their efficacy.