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Publications of Jérôme Darmont

Reference (inproceedings)

D.M. Farid, J. Darmont, N. Harbi, H.H. Nguyen, M. Zahidur, "Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification", International Conference on Computer Systems Engineering (ICCSE 09), Bangkok, Thailand, December 2009; WASET.


In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection
making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.


Attribute selection, Conditional probabilities, information gain, network intrusion detection


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