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Network-Based Anomaly Intrusion Detection Improvement by Bayesian Network and Indirect Relation

  • ByungRae Cha
  • DongSeob Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4693)

Abstract

In this paper, Network-based anomaly intrusion detection method using Bayesian Networks was estimated probability values of behavior contexts based on Bayes theory and Indirect relation. The contexts of network-based FTP service was represented Bayesian Networks of graphic types. We profiled concisely network-based FTP behaviors using behavior context by prior, posterior and Indirect relation. And this method be able to visualize behavior profile to detect/analyze anomaly behavior. We achieve simulation to translate audit data of network into Bayesian network which is network-based behavior profile for anomaly detection.

Keywords

Bayesian Network Packet Data Intrusion Detection Anomaly Detection Intrusion Detection System 
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 2007

Authors and Affiliations

  • ByungRae Cha
    • 1
  • DongSeob Lee
    • 2
  1. 1.Dept. of Computer Eng., Honam Univ.Korea
  2. 2.Dept. of Information & Communication Eng., Honam Univ.Korea

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