Abstract
We discuss the basic intrusion detection techniques, and focus on how to apply association rules to intrusion detection. Begin with analyzing some close relations between user’s behaviors, we discuss the mining algorithm of association rules and apply to detect anomaly in IDS. Moreover, according to the characteristic of intrusion detection, we optimize the mining algorithm of association rules, and use fuzzy logic to improve the system performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Allen J, Christie A, Fithen W,et al. State of the Practice of Instrusion Detection Technologies. CMU/ SEI-99-TR-028. Carnegie Mellon Software Engineering Institute. http://www.cert.org/archive/pdf/ 99tr028.pdf,2001-12-19.
Agrawal R, Imielinski T, Swami A. Mining Association Rules Between Sets of Items in Large Databases.Proceedings of the ACM SIGMOD Conference on Management of Data. http://www. cs. brandeis. edu/∼cs227b/papers/decision-dataminingoverview-sigmod93.pdf.1998-09.
Savasere A, Omiecinski E, Navathe S. An Efficient Algorithm for Mining Association Rules in Large Databases.Proceedings of the 21 st International Conference on Very large Database. http://www. cs.sfu. ca/CC/884/wangk/references/son95. pdf, 1995-01-06.
Cohen E M. Datar E, Fujiwara S,et al. Finding Interesting Associations without Support Pruning.Proc of the 16th Int’l Conf on Data Engineering (ICDE). IEEE, 2001,13(1):64–78.
Information Discovery Inc.OLAP and Data Mining, Bridging the Gap. http://www.it.iitb.ernet.in/∼ sunita/papers/dmkd01.ps, 1998-01-26.
Bridges S M, Vaughn R B.Fuzzy Data Mining and Genetic Algorithms Applied to Intrusion Detection. Mississippi State University: Department of Computer Science, 2000.
Orchard R.FuzzyCLIPS version 6. 04 User’s Guide. National Research Council Canada: Knowledge System Laboratory. http://www.cs.strath.ac.uk/ ∼fabioc/01-ai/docs/fuzzyclips-usrguide.pdf,2001-01-25.
Author information
Authors and Affiliations
Additional information
Foundation item: Supported by the National Natural Science Foundation of China (69983005)
Biography: Mao Ping-ping (1979-), male, Master candidate, research direction: multimedia and network security.
Rights and permissions
About this article
Cite this article
Ping-ping, M., Qiu-ping, Z. Association rules applied to intrusion detection. Wuhan Univ. J. Nat. Sci. 7, 426–430 (2002). https://doi.org/10.1007/BF02828242
Received:
Issue Date:
DOI: https://doi.org/10.1007/BF02828242