By Steven Noel, Duminda Wijesekera (auth.), Daniel Barbará, Sushil Jajodia (eds.)
Data mining is changing into a pervasive know-how in actions as various as utilizing old facts to foretell the luck of a campaign, searching for styles in monetary transactions to find unlawful actions or reading genome sequences. From this attitude, it was once only a subject of time for the self-discipline to arrive the real sector of desktop safety. Applications of information Mining In computing device Security offers a suite of study efforts at the use of knowledge mining in laptop security.
Applications of information Mining In computing device Security concentrates seriously at the use of knowledge mining within the sector 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 incredible candidate for utilizing information mining thoughts. moment, intrusion detection is an exceptionally serious task. This e-book additionally addresses the appliance of knowledge mining to desktop forensics. it is a the most important quarter that seeks to deal with the desires of legislation enforcement in reading the electronic evidence.
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Extra resources for Applications of Data Mining in Computer Security
In Proceedings of the 7th USENIX Security Symposium, San Antonio, TX. , Stolfo, S. , and Mok, K. W. (1999). A data mining framework for building intrusion detection models. In IEEE Symposium on Security and Privacy. Lee, W. and Xiang, D. (2001). Information-theoretic measures for anomaly detection. In IEEE Symposium on Security and Privacy, pages 130143, Oakland, CA. IEEE Computer Society. Liepins, G. and Vaccaro, H. (1989). Anomaly detection purpose and framework. In 12th National Computer Security Co nference, pages 495-504, Baltimore, MD.
In other words, it detects attacks that involve a relatively large number of events within aperiod of time. This is because it sends an alarm only when the number of occurrences of an unexpected rule exceeds a threshold. This limitation is not unique to this approach; most of the anomaly detection models have the same problem (Ning, 2001). 6. Combining data mining and classification for anomaly detection A primary focus of research in anomaly detection is in detecting novel attacks while maintaining sufficiently low numbers of false alarms.
An intrusion-detection model. IEEE Transactions on Software Engineering, 13:222-232. Dowell, C. and Ramstedt, P. (1990). The computerwatch data reduction tool. In 13th National Computer Security Conference, Washington, DC . 27 Engelhardt, D. (1997). Directions for intrusion deteetion and response: A survey. Technical Report DSTO-GD-0155, DSTO Electronics and Surveillance Research Laboratory. , and Pieprzyk, J. (1996). Case-based reasoning for intrusion detection. In 12th Annual Computer Security Applications Conference, San Diego, CA .