An Efficient Feature Selection Algorithm Toward Building Lightweight Intrusion Detection System
You Chen · Chinese Journal of Computers · 2007
Feature selection is one of the most important problems in network security, pattern recognition and data mining areas. For high dimension data, feature selection not only can improve the accuracy and efficiency of classification, but also discover informative subset. This paper proposes a new feature selection algorithm aiming at building lightweight intrusion detection system (IDS) by (1) using a hybrid strategy of genetic algorithm and tabu search (GATS) as search strategy to specify a candidate subset for evaluation; (2) using modified linear Support Vector Machines (SVMs) iterative procedure as wrapper approach to obtain the optimum feature subset. The authors have examined the feasibility of the feature selection algorithm by conducting several experiments on KDD1999 intrusion detection dataset which was categorized as DOS, PROBE, R2L and U2R. The experimental results show that the approach is able not only to speed up the process of selecting important features but also to guarantee high detection rates. Furthermore, the experiments indicate that intrusion detection system with a combination of feature selection algorithm has better performances than that without feature selection algorithm in terms of building time, testing time and detection rates.