Intrusion Detection Method Based on Frequent Pattern

Abstract:

As the surging development of the information technology, Intrusion Detection System has been devised for the safety of computer network. This paper focuses on the method of frequent pattern based intrusion detection. A new formula measuring the normal degree of a transaction is presented. We propose a new algorithm to calculate each transaction’s normal degree as well as detect intrusions. Experiment results show that the proposed algorithm is competent in detecting intrusions with high detection rate and relatively low false positive rate.

• Periodical: Advanced Materials Research (Volumes 204-210)
• Pages: 1751-1754
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.204-210.1751
• Online since: February 2011
• Authors: Jie Yu, Guo Xiang Yao, Wei Wei Zhang
• Keywords: Association Rule, Data Mining (DM), Frequent Pattern, Intrusion Detection

References

[1] W. Lee, S. Stolfo, and K. Mok. A data mining framework for building intrusion detection models. In IEEE Symposium on Security and Privacy, pages 120-132, (1999).

DOI: 10.1109/secpri.1999.766909

[2] W. Lee, K. W. Mok, and S. J. Stolfo. Mining audit data to build intrusion detection models. In Fourth International Conference on Knowledge Discovery and Data Mining, pages 66–72, (1998).

DOI: 10.1145/312129.312212

[3] Jiawei Han, Jian Pei, and Yiwen Yin. Mining Frequent Patterns without Candidate Generation. In Data Mining and Knowledge Discovery, pages 1-8, (2004).

DOI: 10.1023/b:dami.0000005258.31418.83

[4] R. Agrawal, R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB, pages 487–499, (1994).

[5] D. Burdick, M. Calimlim, and J. Gehrke. Mafia: A maximal frequent itemset algorithm for transactional databases. In ICDE, pages 443–452, (2001).

DOI: 10.1109/icde.2001.914857

[6] Zengyou He, and Xiaofei Xu. FP-Outlier: Frequent Pattern Based Outlier Detection. In Computer Science and Information Systems, pages 1-6, (2005).

DOI: 10.2298/csis0501103h

[7] Kazuyo Narita, and Hiroyuki Kitagawa. Outlier Detection for Transaction Databases using Association Rules. In Lecture Notes in Computer Science, pages 1-7, (2008).

[8] T. Imielinski, A. Swami, and R. Agarwal. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD conference on management of data, pages 207 – 216, (1993).

DOI: 10.1145/170036.170072

[9] S.J. Stolfo, et al., 1999. KDD-99 dataset. Available: http: /www. kdd. ics. uci. edu/databases/kddcup99/kddcup99. html.

[10] E-Business & Intelligent Enterprise Computing Research Center. AlphaMiner 2. 0. Available: http: / www. alphaminer. org.

[6] P.G. Clem, M. Rodriguez, J.A. Voigt and C.S. Ashley, U.S. Patent 6, 231, 666. (2001).