Research Article Open Access

From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective

Kok-Chin Khor1, Choo-Yee Ting1 and Somnuk-Phon Amnuaisuk1
  • 1 Faculty of Information Technology, Multimedia University, Cyberjaya, 63100, Selangor, Malaysia


Problem statement: Implementing a single or multiple classifiers that involve a Bayesian Network (BN) is a rising research interest in network intrusion detection domain. Approach: However, little attention has been given to evaluate the performance of BN classifiers before they could be implemented in a real system. In this research, we proposed a novel approach to select important features by utilizing two selected feature selection algorithms utilizing filter approach. Results: The selected features were further validated by domain experts where extra features were added into the final proposed feature set. We then constructed three types of BN namely, Naive Bayes Classifiers (NBC), Learned BN and Expert-elicited BN by utilizing a standard network intrusion dataset. The performance of each classifier was recorded. We found that there was no difference in overall performance of the BNs and therefore, concluded that the BNs performed equivalently well in detecting network attacks. Conclusion/Recommendations: The results of the study indicated that the BN built using the proposed feature set has less features but the performance was comparable to BNs built using other feature sets generated by the two algorithms.

American Journal of Applied Sciences
Volume 6 No. 11, 2009, 1948-1959


Submitted On: 25 September 2009 Published On: 30 November 2009

How to Cite: Khor, K., Ting, C. & Amnuaisuk, S. (2009). From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective. American Journal of Applied Sciences, 6(11), 1948-1959.

  • 11 Citations



  • Network intrusion detection
  • Bayesian classifiers
  • feature selection