Applications of Data Mining in Computer Security by Steven Noel, Duminda Wijesekera (auth.), Daniel Barbará,
By Steven Noel, Duminda Wijesekera (auth.), Daniel Barbará, Sushil Jajodia (eds.)
Data mining is changing into a pervasive expertise in actions as various as utilizing old information to foretell the luck of a campaign, trying to find styles in monetary transactions to find unlawful actions or examining genome sequences. From this angle, it was once only a topic of time for the self-discipline to arrive the real region of laptop safety. Applications of knowledge Mining In machine Security offers a suite of analysis efforts at the use of knowledge mining in computing device security.
Applications of information Mining In computing device Security concentrates seriously at the use of information mining within the quarter of intrusion detection. the cause of this is often twofold. First, the quantity of information facing either community and host task is so huge that it makes it a great candidate for utilizing facts mining innovations. moment, intrusion detection is a very serious task. This ebook additionally addresses the applying of knowledge mining to machine forensics. this can be a the most important region that seeks to handle the desires of legislation enforcement in interpreting the electronic evidence.
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Extra info for Applications of Data Mining in Computer Security
Adaptive, model-based monitoring for cyber attack detection. In Recent Advances in Intrusion Detection, pages 80-93, Toulouse, France. Springer-Verlag. Vigna, G. and Kemmerer, R. A. (1998) . NetSTAT: A Network-based Intrusion Detection Approach. In Proceedings of the International Conference on Knowledge and Data Mining, New York, NY. W. Lee, S. J . S. and Mok, K. W. (1998). Mining audit data to build intrusion detection models. In Proceedings of the International Conference on Knowledge and Data Mining, New York, NY.
However, given the interdisciplinary nature of the topic, it was not possible to write complete introductions to both, intrusion detection and data mining. We assumed that the reader has an intrusion detection background, and consequently put more emphasis on data mining basics. Complementary to this chapter, there is an abundance of excellent introductory material to both intrusion detection (Bace, 2000; Allen et al. , 2001; Berry and Linoff, 1997) that can be consulted if needed. 2. , 1996a; Han and Kamber, 2000).
NetSTAT: A Network-based Intrusion Detection Approach. In Proceedings of the International Conference on Knowledge and Data Mining, New York, NY. W. Lee, S. J . S. and Mok, K. W. (1998). Mining audit data to build intrusion detection models. In Proceedings of the International Conference on Knowledge and Data Mining, New York, NY. Wagner, D. and Dean, R. (2001). Intrusion detection via static analysis. In IEEE Symposium on Security and Privacy. IEEE Computer Society. , and Debara, H. (2000). Intrusion detection using variable-Iength audit trail patterns.