A novel framework for anomaly detection based on hybrid HMM-SVM model

Abstract

Intrusion-detection systems (IDSs) are essential tools for the security of computer systems. Anomaly detection, which uses knowledge about normal behaviors and attempts to detect intrusions by noting significant deviations, has been paid more and more attention. In this paper, we introduce a novel framework for anomaly detection. In the proposed method, two widely used statistical learning method, Hidden Markov Model and Support Vector Machine, are introduced to detect the abnormal events. Then, we fuse the detection results by some special rules. We deploy the method on an IDS system to evaluate its performance, and the experimental results demonstrate that our method can achieve satisfying results. © 2011 IEEE.