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Proceedings Paper

Attribute selection using information gain for a fuzzy logic intrusion detection system
Author(s): Jesús González-Pino; Janica Edmonds; Mauricio Papa
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Paper Abstract

In the modern realm of information technology, data mining and fuzzy logic are often used as effective tools in the development of novel intrusion detection systems. This paper describes an intrusion detection system that effectively deploys both techniques and uses the concept of information gain to guide the attribute selection process. The advantage of this approach is that it provides a computationally efficient solution that helps reduce the overhead associated with the data mining process. Experimental results obtained with a prototype system implementation show promising opportunities for improving the overall detection performance of our intrusion detection system.

Paper Details

Date Published: 18 April 2006
PDF: 10 pages
Proc. SPIE 6241, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006, 62410D (18 April 2006); doi: 10.1117/12.666611
Show Author Affiliations
Jesús González-Pino, Univ. of Tulsa (United States)
Janica Edmonds, Univ. of Tulsa (United States)
Mauricio Papa, Univ. of Tulsa (United States)


Published in SPIE Proceedings Vol. 6241:
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006
Belur V. Dasarathy, Editor(s)

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