Building Lightweight Intrusion Detection System Based on Principal Component Analysis and C4.5 Algorithm

The intrusion detection system deals with huge amount of data which contains irrelevant and redundant features causing slow training and testing process, higher resource consumption as well as poor detection rate. Feature selection, therefore, is an important issue in intrusion detection. An appropriate feature set obtained by feature selection can help to build lightweight intrusion detection system. In this paper, we propose a new hybrid feature selection algorithm based on principal component analysis and C4.5 algorithm to build lightweight intrusion detection system. Our method is able to significantly decrease training and testing times while retaining high detection rates with low false positive rates. We have examined the feasibility of our approach by conducting several experiments using KDD 1999 CUP dataset. The experimental results show that our approach has better performances than those systems listed in the paper in terms of training time, testing time, true positive rate and false positive rate.