A Tabu Clustering algorithm for Intrusion Detection

Authors:
Yong Guo Liu;Xiao Feng Liao;Xue Ming Li;Zhong Fu Wu
Affiliations:
(Correspd. Dept. of Comp. Sci. and Eng., Shanghai Jiaotong Univ., Shanghai 200030, P.R. China. liu-yg@cs.sjtu.edu.cn) Dept. of Comp. Sci. and Eng., Chongqing Univ., Chongqing 400044, China;Department of Computer Science and Engineering, Chongqing University, Chongqing 400044, China;Department of Computer Science and Engineering, Chongqing University, Chongqing 400044, China;Department of Computer Science and Engineering, Chongqing University, Chongqing 400044, China
Venue:
Intelligent Data Analysis
Year:
2004

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Abstract

Traditional methods of intrusion detection lack the extensibility in face of changing network configurations and the adaptability in face of unknown intrusion types. Meanwhile, current machine-learning algorithms for intrusion detection need labeled data to be trained, so they are expensive in computation and sometimes misled by artificial data. In order to solve these problems, a new detection algorithm is proposed in this paper, the Intrusion Detection Based on Tabu Clustering (IDBTC) algorithm. It can automatically set up clusters and detect intrusions by labeling normal and abnormal groups. Computer simulations show that this algorithm is effective for intrusion detection.