Ensemble of classifiers for detecting network intrusion
Proceedings of the International Conference on Advances in Computing, Communication and Control
The use of artificial intelligence based techniques for intrusion detection: a review
Artificial Intelligence Review
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
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Data mining techniques are being applied in building intrusion detection systems to protect computing resources against unauthorised access. In this paper, the performance of three well known data mining classifier algorithms namely, ID3, J48 and Naïve Bayes are evaluated based on the 10-fold cross validation test. Experimental results using the KDDCup’99 IDS data set demonstrate that while Naïve Bayes is one of the most effective inductive learning algorithms, decision trees are more interesting as far as the detection of new attacks is concerned.