Intrusion Detection System Based on K-Star Classifier and Feature Set Reduction

Abstract

Network security and Intrusion Detection Systems (IDS's) is an important security related research area. This paper applies K-star algorithm with filtering analysis in order to build a network intrusion detection system. For our experimental analysis and as a case study, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 66.0% for the training set and the remainder for the testing set a 2 class classifications has been implemented. WEKA which is a java based open source software consists of a collection of machine learning algorithms for Data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with other existing approaches used for efficient network intrusion detection.