Network Intrusion Detection Using Tree Augmented Naive-Bayes

Computer networks are nowadays subject to an increasing number of attacks. Intrusion Detection Systems (IDS) are designed to protect them by identifying malicious behaviors or improper uses. Since the scope is different in each case (register already-known menaces to later recognize them or model legitimate uses to trigger when a variation is detected), IDS have failed so far to respond against both kind of attacks. In this paper, we apply two of the efficient data mining algorithms called Naive Bayes and tree augmented Naive Bayes for network intrusion detection and compare them with decision tree and support vector machine. We present experimental results on NSL-KDD data set and then observe that our intrusion detection system has higher detection rate and lower false positive rate.