A new intrusion detection system using support vector machines and hierarchical clustering

Author:

Khan Latifur,Awad Mamoun,Thuraisingham Bhavani

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems

Reference42 articles.

1. Agarwal, D.K.: Shrinkage estimator generalizations of proximal support vector machines, In: Proceedings of the 8th International Conference Knowledge Discovery and Data Mining, pp. 173–182. Edmonton, Canada (2002)

2. Anderson, D., Frivold, T., Valdes, A.: Next-generation intrusion detection expert system (NIDES): a summary. Technical Report SRI-CSL-95-07. Computer Science Laboratory, SRI International, Menlo Park, CA (May 1995)

3. Axelsson, S.: Research in intrusion detection systems: a survey. Technical Report TR 98-17 (revised in 1999). Chalmers University of Technology, Goteborg, Sweden (1999)

4. Balcazar, J.L., Dai, Y., Watanabe, O.: A random sampling technique for training support vector machines for primal-form maximal-margin classifiers, algorithmic learning theory. In: Proceedings of the 12th International Conference, ALT 2001, p. 119. Washington, DC (2001)

5. Bivens, A., Palagiri, C., Smith, R., Szymanski, B., Embrechts, M.: Intelligent engineering systems through artificial neural networks. In: Proceedings of the ANNIE-2002, vol. 12, pp. 579–584. ASME Press, New York (2002)

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