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 turning into a pervasive know-how in actions as diversified as utilizing ancient information to foretell the good fortune of a campaign, trying to find styles in monetary transactions to find unlawful actions or studying genome sequences. From this angle, it used to be only a topic of time for the self-discipline to arrive the $64000 sector of computing device safety. Applications of knowledge Mining In laptop Security provides a set of analysis efforts at the use of knowledge mining in desktop security.

Applications of knowledge Mining In computing device Security concentrates seriously at the use of information mining within the quarter of intrusion detection. the cause of this can be twofold. First, the quantity of knowledge facing either community and host job is so huge that it makes it an excellent candidate for utilizing facts mining recommendations. moment, intrusion detection is a very severe job. This publication additionally addresses the appliance of information mining to laptop forensics. this can be a the most important region that seeks to deal with the wishes of legislation enforcement in studying the electronic evidence.

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2000) . Intrusion detection using autonomous agents. Computer N etworks, 34(4) :547-570. , Crawford, R. , and Zerkle, D. (1996). GrIDS-A Graph Based Intrusion Detection System for Large Networks. In 19th National Information Systems Security Conference, pages 361-370, Baltimore, MD. NIST and NSA. Vaccaro, H. and Liepins, G. (1989). Detection of anomalous computer session activity. In IEEE Symposium on Security and Privacy. IEEE Computer Society. Valdes, A. and Skinner, K. (2000). Adaptive, model-based monitoring for cyber attack detection.

3 concludes the discussion by summarizing several open research challenges in the field of data mining. 1 Data Mining, KDD, and Related Fields The term data mining is frequently used to designate the process of extracting useful information from large databases. In this chapter, we adopt a slightly different view, which is identical to the one expressed by Fayyad et al. (1996b, Chapter 1) 1 . In this view, the term knowledge discovery in databases (KDD) is used to denote the process of extracting useful knowledge from large data sets.

The data mining literature contains several variants of frequent episode rules (Mannila et al. , 1997; Lee et al. , 1998). e. at approximately the same time). 2) where P , Q, and Rare predicates over a user-defined dass of admissible predicates (Hätönen et al. , 1996) . Intuitively, this rule says that two records that satisfy P and Q, respectively, are generally accompanied by a third record that satisfies R . The parameters s, c, and ware called support, confidence, and window width, and their interpretation in this context is as follows: The support s is the probability that a time window of w seconds contains three records p, q, and r that satisfy P, Q, and R, respectively.

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