In order to improve the detection precision and convergence rate of the intrusion detection algorithm for wireless sensor networks（ WSNs） based on fusion of particle swarm optimization（ PSO） algorithm and support vector machine（ SVM）

a WSNs intrusion detection algorithm based on complete sine-mapping chaotic CS-CPSO-SVM based on fusion of CS-CPSO and SVM is proposed. Chaotic PSO algorithm based on CS-CPSO is used to optimize the parameter of SVM

and the sine-mapping chaotic search is applied to not only the generation of initial population and chaotic perturbation of local optimal for PSO algorithm

but also the optimization of the inertia weight and the generation of the random constant and the learning factor

moreover

multiple initial values are used to generate a number of chaotic orbits. With the KDDCUP99 data set as experimental data

the oretical analysis and simulation results show that the proposed method can effectively detect the intrusion behavior

and has a good detection precision and convergence speed.