Significance of Hybrid Feature Selection Technique for Intrusion Detection Systems

Abstract

Intrusion detection is the need of technical world where data is generating and changing at a very rapid rate. In last decade feature selection is the science that has given a new perspective to research in the area of Intrusion Detection System. Objective of this paper is to perform an analysis and comparison of various feature selection techniques with a new technique of hybrid Particle Swarm Optimization (PSO). Statistical Analysis: In this paper well known filter and wrapper feature selection techniques have been explored along with a hybrid PSO technique on the standard KDDCup99 dataset. A comparative analysis is performed over four filter techniques and two wrapper based techniques. Four different classifiers are compared to select the one providing good accuracy on the dataset. Findings: The hybrid PSO feature selection technique gives significant improvement in prediction capability as compared to traditional feature selection approaches. Analysis shows that SVM classifier provides better classification results. SVM is used as classifier because of its high accuracy. The analysis over 4 filter and two wrapper techniques shows that Hybrid PSO provides better results with 98.6% accuracy and 24 feature subset. Application/Improvements: Analysis provides importance of hybrid PSO, which may be applied to not only intrusion detection but also various other areas where feature reduction is required.