Modeling program behaviors by hidden markov models for intrusion detection

Wei Wang, Xiao Hong Guan, Xiang Liang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

49 Scopus citations


Intrusion detection is an important technique in the defense-in-depth network security framework and a hot topic in computer network security in recent years. In this paper, a new efficient intrusion detection method based on Hidden Markov Models (HMMs) is presented. HMMs are applied to model the normal program behaviors using traces of system calls issued by processes. The output probability of a sequence of system calls is calculated by the normal model built If the probability of a sequence in a trace is below a certain threshold, the sequence is flagged as a mismatch. If the ratio between the mismatches and all the sequences in a trace exceeds another threshold, the trace is then considered as a possible intrusion. The method is implemented and tested on the sendmail system call data from the University of New Mexico. Experimental results show that the performance of the proposed method in intrusion detection is better than Other methods.
Original languageEnglish (US)
Title of host publicationProceedings of 2004 International Conference on Machine Learning and Cybernetics
Number of pages6
StatePublished - Nov 2 2004
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-20


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