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Privacy Preserving Sequential Patterns Mining for Network Traffic Data  

Kim, Seung-Woo (연세대학교 컴퓨터과학과)
Park, Sang-Hyun (연세대학교 컴퓨터과학과)
Won, Jung-Im (한양대학교 정보통신대학)
Publication Information
Journal of KIISE:Databases / v.33, no.7, 2006 , pp. 741-753 More about this Journal
Abstract
As the total amount of traffic data in network has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical privacy preserving sequential pattern mining method on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model and the retention replacement technique. In addition, our method accelerates the overall mining process by maintaining the meta tables so as to quickly determine whether candidate patterns have ever occurred. The various experiments with real network traffic data revealed tile efficiency of the proposed method.
Keywords
Data mining; Sequential pattern; Network traffic; Privacy;
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1 R. Agrawal, R. Srikant, and D. Thomas, 'Privacy Preserving OLAF,' In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 251-262, 2005   DOI
2 J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. Hsu, 'FreeSpan: Frequent pattern-projected sequential pattern mining,' In Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining, pp. 355-359, 2000   DOI
3 J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, 'Mining Sequential Patterns by Pattern-growth: The PrefixSpan Approach,' IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 11, pp. 1424-1440, 2004   DOI   ScienceOn
4 H. Kum, J. Pei, W. Wang, and D. Duncan, 'ApproxMAP: Approximate Mining of Consensus Sequential Patterns,' In Proceedings of the 3rd SIAM International Conference on Data Mining, pp, 311-315, 2003
5 M. Garofalakis, R. Rastogi, and K. Shim, 'SPIRIT: Sequential Pattern Mining with Regular Expression Constraints,' In Proceedings of 25th International Conference on Very Large Data Bases Conference, pp. 223-234, 1999
6 J. Pei, J. Han, and W. Wang, 'Mining Sequential Patterns with Constraints in Large Databases,' In Proceedings of the 11th Conference on Information and Knowledge Management, pp. 18-25, 2002   DOI
7 M. Kantarcioglu and C. Clifton, 'Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data,' In Proceedings of the 2002 ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pp. 24-31, 2002
8 R. Srikant and R. Agrawal, 'Mining Sequential Patterns: Generalizations and performance improvements,' In Proceedings of the 5th International Conference on Extending Database Technology, pp. 3-17, 1996
9 J. Zhan, L. Chang, and S. Matwin, 'Privacy-Preserving Collaborative Sequential Pattern Mining,' In Proceedings of Workshop on Link Analysis, Counter-terrorism and Privacy in conjunction with SIAM International Conference on Data Mining, pp. 61-72, 2004
10 R. Agrawal and R. Srikant, 'Mining Sequential Patterns,' In Proceedings of the 11th International Conference on Data Engineering, pp. 3-14, 1995   DOI
11 C. Clifton and D. Marks, 'Security and Privacy Implication of Data Mining,' In Proceedings of the 1996 ACM Workshop on Data Mining and Knowledge Discovery, pp. 15-19, 1996
12 S. Song, Z. Huang, H. Hu, and S. Jin, 'A Sequential Pattern Mining Algorithm for Misuse Intrusion Detection,' In Proceedings of International Workshop on Information Security and Survivability for Grid, pp. 458-465, 2004
13 S. Rizvi and J. Haritsa, 'Maintaining Data Privacy in Association Rule Mining,' In Proceedings of the 28th Conference on Very Large Data Base, pp. 682-693, 2002
14 R. Agrawal and R. Srikant, 'Privacy-Preserving Data Mining,' In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp, 439-450, 2000   DOI
15 A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke 'Privacy Preserving Mining of Association Rules,' In Proceedings of the 2002 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217-228, 2002   DOI
16 Y. Hu and B. Panda, 'A Data Mining Approach for Database Intrusion Detection,' In Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 711-716, 2004   DOI
17 F. Masseglia, P. Poncelet, and M. Teisseire, 'Incremental Mining of Sequential Patterns in Large Databases,' Data and Knowledge Engineering, Vol. 46, Issue 1, pp. 97-121, 2003   DOI   ScienceOn
18 P. Dokas, L. Ertoz, V. Kumar, A. Lazarevic, J. Srivastava, and P. Tan, 'Data Mining for Network Intrusion Detection,' In Proceedings of NSF Workshop on Next Generation Data Mining, pp. 73-81, 2002
19 J. Luo and S. Bridges, 'Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection,' International Journal of Intelligent Systems, Vol. 15, No.8, pp. 687-704, 2000   DOI   ScienceOn
20 W. Lee, S. Stolfo, and K. Mok, 'A Data Mining Framework for Building Intrusion Detection Models,' In Proceedings of IEEE Symposium on Security and Privacy, pp. 120-132, 1999   DOI