A Comparative Study of Data Mining Algorithms for Network Intrusion Detection

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 Naive Bayes are evaluated based on the 10-fold cross validation test. Experimental results using the KDDCuppsila99 IDS data set demonstrate that while Naive 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.