Network intrusion detection using feature selection and Decision tree classifier

Security of computers and the networks that connect them is increasingly becoming of great significance. Machine learning techniques such as Decision trees have been applied to the field of intrusion detection. Machine learning techniques can learn normal and anomalous patterns from training data and generate classifiers that are used to detect attacks on computer system. In general the input to classifiers is in a high dimension feature space, but not all features are relevant to the classes to be classified. Feature selection is a very important step in classification since the inclusion of irrelevant and redundant features often degrade the performance of classification algorithms both in speed and accuracy. In this paper, we have considered three different approaches for feature selection, Chi square, Information Gain and ReliefF which is based on filter approach. A comparative study of the three approaches is done using decision tree as classifier. The KDDcup 99 data set is used to train and test the decision tree classifiers.