Building Efficient Intrusion Detection Model Based on Principal Component Analysis and C4.5

An appropriate feature set helps to build efficient decision model as well as reduced feature set lights up the training and testing process considerably. In this paper, we propose a new approach to build efficient Intrusion Detection System (IDS) based on principal component analysis and C4.5. Our method is able to significantly decrease training and testing times while retaining high detection rates with low false positives rates as well as stable feature selection results. We have examined the feasibility of our approach by conducting several experiments using KDD 1999 CUP intrusion dataset. The experimental results show the feasibility of our approach to enable one to building efficient IDS.