1. A Tabu Clustering algorithm for Intrusion Detection
- Author
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Xue Ming Li, Xiao Feng Liao, Zhong Fu Wu, and Yong Guo Liu
- Subjects
Anomaly-based intrusion detection system ,Computer science ,Computation ,media_common.quotation_subject ,Intrusion detection system ,computer.software_genre ,Adaptability ,Tabu search ,Theoretical Computer Science ,Set (abstract data type) ,Artificial Intelligence ,Face (geometry) ,Computer Vision and Pattern Recognition ,Data mining ,Cluster analysis ,computer ,media_common - 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.
- Published
- 2004
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