Selection and detection of network intrusion feature based on BPSO-SVM

In the proposed algorithm, every particle in the swarm stands for a selected subset of features. 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. Thus through particle swarm optimization, the intrusion feature selection and classification can be achieved. A probabilistic mutation of BPSO is adopted to avoid local optimal and a tabu search table is used to enlarge particle swarm's search space and avoid repeated computation. The results of experiment demonstrate that applying a hybrid of BPSO-SVM in intrusion detection system can be an effective way for feature selection and intrusion detection via using the data sets of KDD cup 99.