Comparison of classification techniques for intrusion detection dataset using WEKA

As the network based applications are growing rapidly, the network security mechanisms require more attention to improve speed and precision. The ever evolving new intrusion types pose a serious threat to network security. Although numerous network security tools have been developed, yet the fast growth of intrusive activities is still a serious issue. Intrusion detection systems (IDSs) are used to detect intrusive activities on the network. Machine learning and classification algorithms help to design “Intrusion Detection Models” which can classify the network traffic into intrusive or normal traffic. In this paper we present the comparative performance of NSL-KDD based data set compatible classification algorithms. These classifiers have been evaluated in WEKA (Waikato Environment for Knowledge Analysis) environment using 41 attributes. Around 94,000 instances from complete KDD dataset have been included in the training data set and over 48,000 instances have been included in the testing data set. Garrett's Ranking Technique has been applied to rank different classifiers according to their performance. Rotation Forest classification approach outperformed the rest.