Feature selection method for network intrusion based on GQPSO attribute reduction
Shangfu Gong, Gong Xingyu, Xiaoru Bi · 2011
Aiming to problem of classification algorithm with low detection speed and low detection rate in high dimensional network data intrusion detection. A novel approach for feature selection based on Genetic Quantum Particle Swarm Optimization(GQPSO) attribute reduction in network intrusion detection is proposed in the paper. In the approach, selection and variation of genetic algorithm with QPSO algorithm are combined to form GQPSO algorithm; normalized mutual information between attributes defined as GQPSO algorithm fitness function to guide it's reduction of attributes to realize optimal selection of network data feature subset. KDD99 data-set are used to experiment. The experimental result shows that the approach is more effective than QPSO and PSO algorithms in discarding independent and redundancy attributes. As a result, intrusion detection rate and speed of classification algorithm are greatly heightened by using the method.