SCIDS: a soft computing intrusion detection system

Authors:
Ajith Abraham;Ravi Jain;Sugata Sanyal;Sang Yong Han
Affiliations:
School of Computer Science and Engineering, Chung-Ang University, Korea;Land Operations Division, Defence Science & Technology Organisation (DSTO), Australia;School of Technology and Computer Science, Tata Institute of Fundamental Research, India;School of Computer Science and Engineering, Chung-Ang University, Korea
Venue:
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
Year:
2004

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Abstract

An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This paper evaluates three fuzzy rule based classifiers for IDS and the performance is compared with decision trees, support vector machines and linear genetic programming. Further, Soft Computing (SC) based IDS (SCIDS) is modeled as an ensemble of different classifiers to build light weight and more accurate (heavy weight) IDS. Empirical results clearly show that SC approach could play a major role for intrusion detection.