Quantum Particle swarm optimization based network Intrusion feature selection and Detection

Abstract

Considering the relevance among features, which filter-based feature selection method fails to deal with, a kind of hybrid quantum particle swarm optimization and support vector machines based network intrusion feature selection wrapper algorithm is put forward. The subset of features is represented using quantum superposition characteristic and probability representation, among which superposition characteristic can make a single particle represent several states, thus potentially increases population diversity. Every particle in the quantum particle swarm stands for a selected subset of features. A probabilistic mutation is adopted to avoid local optimal and a taboo search table is used to enlarge particle swarm's search space and avoid repeated computation. The fitness of particle is defined as the correct classification percentage by SVM using a training set whose patterns are represented using only the selected subset of features. The results of experiments demonstrate that the proposed method can be an effective and efficient way for feature selection and detection via using the data sets of KDD cup 99.