A Tabu Clustering algorithm for Intrusion Detection

Article type: Research Article

Authors: Liu, Yong Guo^{; *} | Liao, Xiao Feng | Li, Xue Ming | Wu, Zhong Fu

Affiliations: Department of Computer Science and Engineering, Chongqing University, Chongqing 400044, China

Correspondence: [*] Corresponding author: Y. Liu, Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030, P.R. China. E-mail: [email protected]

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.

Keywords: intrusion detection, clustering, tabu search

DOI: 10.3233/IDA-2004-8402

Journal: Intelligent Data Analysis, vol. 8, no. 4, pp. 325-344, 2004