A Hybrid PSO-BPSO Based Kernel Extreme Learning Machine Model for Intrusion Detection

With the success of the digital economy and the rapid development of its technology, network security hasreceived increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine(KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together todetermine the parameter combination and the feature subset. A fitness function based on the detection rate andthe number of selected features is proposed. The results show that the method can simultaneously determinethe parameter values and select features. Furthermore, competitive or better accuracy can be obtained usingapproximately one quarter of the raw input features. Experiments proved that our method is slightly better thanthe genetic algorithm-based KELM model. KCI Citation Count: 0