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High-Order Markov Kernels for Network Intrusion Detection

  • Shengfeng Tian
  • Chuanhuan Yin
  • Shaomin Mu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

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 kernel are adopted as the anomaly detectors. This approach solves the problem of high dimension parameter space. Experiments show that this system can produce good detection performance with low computational overhead.

Keywords

Support Vector Machine Hide Markov Model Intrusion Detection Anomaly Detection System Call 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shengfeng Tian
    • 1
  • Chuanhuan Yin
    • 1
  • Shaomin Mu
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingP.R. China

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