Abstract
Intrusion detection systems (IDSs) are currently drawing a great amount of interest as a key part of system defence. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. Recently, machine learning methodologies are playing an important role in detecting network intrusions (or attacks), which further helps the network administrator to take precautionary measures for preventing intrusions. In this paper, we propose to use ten machine learning approaches that include Decision Tree (J48), Bayesian Belief Network, Hybrid Naïve Bayes with Decision Tree, Rotation Forest, Hybrid J48 with Lazy Locally weighted learning, Discriminative multinomial Naïve Bayes, Combining random Forest with Naïve Bayes and finally ensemble of classifiers using J48 and NB with AdaBoost (AB) to detect network intrusions efficiently. We use NSL-KDD dataset, a variant of widely used KDDCup 1999 intrusion detection benchmark dataset, for evaluating our proposed machine learning approaches for network intrusion detection. Finally, Experimental results with 5-class classification are demonstrated that include: Detection rate, false positive rate, and average cost for misclassification. These are used to aid a better understanding for the researchers in the domain of network intrusion detection.