Feature Selection of the Intrusion Detection Data Based on Particle Swarm Optimization and Neighborhood Reduction

Feature selection, also called attribute selection, is the process of selecting a subset of features from a relatively large dataset. Feature selection is efficient in removing redundant and noisy features and the selected features are effective to describe the whole dataset. Since there are many features in intrusion detection data, which is large in quantity, feature selection plays an important role in intrusion detection. Within these features, many are numerical features. The traditional feature selection methods, such as the correlation analysis, information gain (IG), support vector machine (SVM) and rough set, must discretize numerical features when dealing with mixed features. The discretization process usually consumes much time and sometimes will even result in the loss of important information, decreasing the classification accuracy. Aiming at solving these problems, the neighborhood rough set reduction model is employed in this paper, which can process the numerical features directly without discretization. Then the particle fitness function in particle swarm optimization (PSO) algorithm is built based on that model. Finally, a novel feature selection algorithm based on particle swarm optimization and neighborhood rough set reduction model is proposed. Experimental results prove that the new algorithm improves classification ability with fewer features selected.