A Tabu Clustering algorithm for Intrusion Detection
Yong Guo Liu, Xiao Feng Liao, Xue Ming Li, Zhong Fu Wu · Intelligent Data Analysis · 2004
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 comp utation 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.