An Intrusion Detection Method Based on Hierarchical Hidden Markov Models

查看全文 作　　者：JIA [1,2]Chunfu;YANG [2]Feng

机构地区：[1]College of Information Science and Technology, Universityof Science and Technology of China, Hefei 230026, Anhui, China;;[2]College of Information Technology and Science, NankaiUniversity, Tianjin 300071, China

出　　处：《Wuhan University Journal of Natural Sciences》索引2007年第12卷第1期,共4页

基　　金：Supported by the Science and Technology Development Project Foundation of Tianjin (033800611, 05YFGZGX24200) 摘　　要：This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hier- archical hidden Markov model (HHMM) is used to represent a temporal profile of normal behavior in a computer system. The HHMM of the norm profile is learned from historic data of the system’s normal behavior. The observed behavior of the system is analyzed to infer the probability that the HHMM of the norm pro- file supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The model was implemented and tested on the UNIX system call sequences collected by the University of New Mexico group. The testing results showed that the model can clearly iden- tify the anomaly activities and has a better performance than hid- den Markov model. 关键词：计算机网络技术隐马尔可夫模型安全技术

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An Intrusion Detection Method Based on Hierarchical Hidden Markov Models

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