Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Machine learning techniques for the computer security domain of anomaly detection
Machine learning techniques for the computer security domain of anomaly detection
Processing of massive audit data streams for real-time anomaly intrusion detection
Computer Communications
Sequence alignment for masquerade detection
Computational Statistics & Data Analysis
Masquerade Detection Using Command Prediction and Association Rules Mining
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
Practical User Identification for Masquerade Detection
WCECS '08 Proceedings of the Advances in Electrical and Electronics Engineering - IAENG Special Edition of the World Congress on Engineering and Computer Science 2008
A prototype real-time intrusion-detection expert system
SP'88 Proceedings of the 1988 IEEE conference on Security and privacy
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Masquerade detection is now one of the major concerns of system security research and its difficulty is to model user behavior on the nonstationary audit data. Many previous works represent the user behavior based on fixed-length models. In this paper, we propose a variable-length model to overcome their weakness in the precision and adaptability of user profiling. In the model, the user's normal behavior is profiled by Markov chain with states of variable-length sequences. At first multiple shell command streams of different lengths are generated and different shell command sequences are hierarchically merged into several sets to form the library of general sequences. Then the variable-length behavioral patterns of a valid user are mined and the Markov chain is constructed. While performing detection, the probabilities of short state sequences are calculated, smoothed with sliding windows, and finally used to classify the monitored user's activity as normal or abnormal. Our experiments with standard datasets such as Purdue data and SEA data reveal that the proposed model can achieve higher detection accuracy, require less memory and take shorter time than the other traditional methods and is amenable for real-time intrusion detection.