Making large-scale support vector machine learning practical
Advances in kernel methods
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Anomaly Detection Enhanced Classification in Computer Intrusion Detection
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Intrusion detection using sequences of system calls
Journal of Computer Security
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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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It is required to realize practically useful masquerade detection for secure environments. In this paper, we propose a new masquerade detection method, which is based on support vector machine and using co-occurrence matrix. Our method can be performed with low cost and achieve good detection rate. We also consider online update for adapting to changes of modeled users' behaviors. We report some experimental results showing our method would be able to work well in real situations