A Tabu Clustering algorithm for Intrusion Detection
作者： Yong Guo Liu , Xiao Feng Liao , Xue Ming Li , Zhong Fu Wu
关键词: Set (abstract data type) 、 Intrusion detection system 、 Computation 、 Adaptability 、 Anomaly-based intrusion detection system 、 Tabu search 、 Computer science 、 Face (geometry) 、 Cluster analysis 、 Data mining
摘要: Traditional methods of intrusion detection lack the extensibility in face changing network configurations and adaptability unknown types. Meanwhile, current machine-learning algorithms for need labeled data to be trained, so they are expensive computation sometimes misled by artificial data. In order solve these problems, a new algorithm is proposed this paper, Intrusion Detection Based on Tabu Clustering (IDBTC) algorithm. It can automatically set up clusters detect intrusions labeling normal abnormal groups. Computer simulations show that effective detection.
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