High-order Markov kernels for intrusion detection

Abstract

In intrusion detection systems, sequences of system calls executed by running programs can be used as evidence to detect anomalies. Markov chain is often adopted as the model in the detection systems, in which high-order Markov chain model is well suited for the detection, but as the order of the chain increases, the number of parameters of the model increases exponentially and rapidly becomes too large to be estimated efficiently. In this paper, one-class support vector machines (SVMs) using high-order Markov kernels are adopted as the anomaly detectors. This approach solves the problem of high-dimension parameter space. Furthermore, a rapid algorithm based on suffix tree is presented for the computation of Markov kernels in linear time. Experimental results show that the SVM with Markov kernels can produce good detection performance with low computational cost.