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檢索了，檢索信息如下 Accession number: 20110213577118 Title: Intrusion detection based on fuzzy association rules Authors: Wu, KaiXing1 ; Hao, Juan1 ; Wang, Chunhua1 Author affiliation: 1 Department of Information and Eletronic Engineering, Handan, Hebei Province, China Corresponding author: Wu, K. (haojuanjsj@163.com) Source title: Proceedings - 2010 International Symposium on Intelligence Information Processing and Trusted Computing, IPTC 2010 Abbreviated source title: Proc. - Int. Symp. Intell. Inf. Process. Trusted Comput., IPTC Monograph title: Proceedings - 2010 International Symposium on Intelligence Information Processing and Trusted Computing, IPTC 2010 Issue date: 2010 Publication year: 2010 Pages: 200-203 Article number: 5663068 Language: English ISBN-13: 9780769541969 Document type: Conference article (CA) Conference name: 2010 International Symposium on Intelligence Information Processing and Trusted Computing, IPTC 2010 Conference date: October 28, 2010 - October 29, 2010 Conference location: Huanggang, China Conference code: 83425 Sponsor: Wuhan University; Huanggang Normal University; IEEE Wuhan Section; IEEE Signal Processing Tainan Chapter Publisher: IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States Abstract: With the rapid development of computer network technology, network not only provides the service for the people, but also has brought many negative effects. Intrusion detection is used to solve this problem. In order to improve the speed and intensity of intrusion detection, data mining technology can be applied to intrusion detection systems. Association rules are a common method in data mining. But, it causes the sharp boundary problem. The concept of fuzzy set is better than partition method because fuzzy sets provide a smooth transition between members and non-members of a set, consequently handle the sharp boundary problem in an appropriate way. In this paper, fuzzy association rules is researched in Intrusion Detection System. And Intrusion Detection framework is designed. It outperforms other methods, especially in terms of false positive rate. © 2010 IEEE. Number of references: 7 |
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