Analysis of Feature Selection and Ensemble Classifier Methods for Intrusion Detection

Day by day network security is becoming more challenging task. Intrusion detection systems (IDSs) are one of the methods used to monitor the network activities. Data mining algorithms play a major role in the field of IDS. NSL-KDD'99 dataset is used to study the network traffic pattern which helps us to identify possible attacks takes place on the network. The dataset contains 41 attributes and one class attribute categorized as normal, DoS, Probe, R2L and U2R. In proposed methodology, it is necessary to reduce the false positive rate and improve the detection rate by reducing the dimensionality of the dataset, use of all 41 attributes in detection technology is not good practices. Four different feature selection methods like Chi-Square, SU, Gain Ratio and Information Gain feature are used to evaluate the attributes and unimportant features are removed to reduce the dimension of the data. Ensemble classification techniques like Boosting, Bagging, Stacking and Voting are used to observe the detection rate separately with three base algorithms called Decision stump, J48 and Random forest.