Intrusion Detection Using Variable-Length Audit Trail Patterns
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
Learning Program Behavior Profiles for Intrusion Detection
Proceedings of the Workshop on Intrusion Detection and Network Monitoring
Anomaly Detection Using Call Stack Information
SP '03 Proceedings of the 2003 IEEE Symposium on Security and Privacy
A Fast Automaton-Based Method for Detecting Anomalous Program Behaviors
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
A sense of self for Unix processes
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
Probabilistic techniques for intrusion detection based on computer audit data
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Anomaly detection has emerged as an important approach to computer security. In this paper, a new anomaly detection method based on Hidden Markov Models (HMMs) is proposed to detect intrusions. Both system calls and return addresses from the call stack of the program are extracted dynamically to train and test HMMs. The states of the models are associated with the system calls and the observation symbols are associated with the sequences of return addresses from the call stack. Because the states of HMMs are observable, the models can be trained with a simple method which requires less computation time than the classical Baum-Welch method. Experiments show that our method reveals better detection performance than traditional HMMs based approaches.