Hybrid Feature Selection Based on Improved GA for the Intrusion Detection System - Shu-xin Zhu; Bin Hu (Institute of Advanced Engineering and Science, 2013)
High dimensionality is one of the most troublesome difficulties encountered in intrusion detection system analysis and application. For high dimension data, feature selection not only can improve the accuracy and efficiency of classification, but also discover informative subset. Combining Filter type and Wrapper type characteristics, this paper proposes a hybrid type method for feature selection using a improved genetic algorithm contained reward and punishment mechanism. The mechanism can guarantee this algorithm rapid convergence on approximate global optimal solution. According to the experimental results, this algorithm performs well and it's time complexity is low. Keywords: intrusion detection system; genetic algorithm(GA); Feature selection; Mutual information; hybrid type.DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.1823