Decision Tree Techniques Applied on NSL-KDD Data and Its Comparison with Various Feature Selection Techniques

Intrusion detection system (IDS) is one of the important research area in field of information and network security to protect information or data from unauthorized access. IDS is a classifier that can classify the data as normal or attack. In this paper, we have focused on many existing feature selection techniques to remove irrelevant features from NSL-KDD data set to develop a robust classifier that will be computationally efficient and effective. Four different feature selection techniques :Info Gain, Correlation, Relief and Symmetrical Uncertainty are combined with C4.5 decision tree technique to develop IDS . Experimental works are carried out using WEKA open source data mining tooland obtained results show that C4.5 with Info Gain feature selection technique has produced highest accuracy of 99.68% with 17 features, however result obtain in case of Symmetrical Uncertainty with C4.5 is also promising with 99.64% accuracy in case of only 11 features . Results are better as compare to the work already done in this area.