An evaluation of data mining classification models for network intrusion detection
Chakchai So–In, Nutakarn Mongkonchai, Phet Aimtongkham, Kasidit Wijitsopon, Kanokmon Rujirakul · 2014
Due to a rapid growth of Internet, the number of network attacks has risen leading to the essentials of network intrusion detection systems (IDS) to secure the network. With heterogeneous accesses and huge traffic volumes, several pattern identification techniques have been brought into the research community. Data Mining is one of the analyses which many IDSs have adopted as an attack recognition scheme. Thus, in this paper, the classification methodology including attribute and data selections was drawn based on the well-known classification schemes, i.e., Decision Tree, Ripper Rule, Neural Networks, Naïve Bayes, k-Nearest-Neighbour, and Support Vector Machine, for intrusion detection analysis using both KDD CUP dataset and recent HTTP BOTNET attacks. Performance of the evaluation was measured using recent Weka tools with a standard cross-validation and confusion matrix.