ADDING EXPERT KNOWLEDGE TO TAN-BASED INTRUSION DETECTION SYSTEMS

S. Benferhat, A. Boudjelida, H. Drias

Abstract

Bayesian networks are important knowledge representation tools for handling uncertain pieces of information. The success of these models is strongly related to their capacity to represent and handle (in)dependence relations. A simple form of Bayesian networks, called naive Bayes has been successively applied in many classification tasks. In particular, naive Bayes have been used for intrusion detection. Unfortunately, naive Bayes are based on a strong independence assumption that limits its application scope. This paper considers the well-known Tree Augmented Naïve Bayes (TAN) classifiers in the context of intrusion detection. In particular, we study how additional expert information such that “it is expected that 80% of traffic will be normal” can be integrated in classification tasks. Experimental results show that our approach improves existing results.

References

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Paper Citation


in Harvard Style

Benferhat S., Boudjelida A. and Drias H. (2009). ADDING EXPERT KNOWLEDGE TO TAN-BASED INTRUSION DETECTION SYSTEMS . In Proceedings of the International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2009) ISBN 978-989-674-005-4, pages 61-64. DOI: 10.5220/0002262200610064


in Bibtex Style

@conference{secrypt09,
author={S. Benferhat and A. Boudjelida and H. Drias},
title={ADDING EXPERT KNOWLEDGE TO TAN-BASED INTRUSION DETECTION SYSTEMS},
booktitle={Proceedings of the International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2009)},
year={2009},
pages={61-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002262200610064},
isbn={978-989-674-005-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2009)
TI - ADDING EXPERT KNOWLEDGE TO TAN-BASED INTRUSION DETECTION SYSTEMS
SN - 978-989-674-005-4
AU - Benferhat S.
AU - Boudjelida A.
AU - Drias H.
PY - 2009
SP - 61
EP - 64
DO - 10.5220/0002262200610064