This paper addresses the taskof detecting intrusions in the form of malicious attacks on programs running on a host computer system by inspecting the trace of system calls made by these programs. We use ‘attack-tree’ type generative models for such intrusions to select features that are used by a Support Vector Machine Classifier. Our approach combines the ability of an HMM generative model to handle variable-length strings, i.e. the traces, and the non-asymptotic nature of Support Vector Machines that permits them to workw ell with small training sets.
CITATION STYLE
Baras, J. S., & Rabi, M. (2002). Intrusion detection with support vector machines and generative models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2433, pp. 32–47). Springer Verlag. https://doi.org/10.1007/3-540-45811-5_3
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