An Optimization Method for Parameters of SVM in Network Intrusion Detection System

Network intrusion detection based on SVM is the hot topic of network security research, and the existing researches have low detection rate, high false positive rate and other issues. Optimizing particle swarm optimization parameters of SVM is an effective solution, but the PSO algorithm is easy to fall into local optimum and results premature convergence. We propose an improved particle swarm optimization algorithm ICPSO, which use chaos operator ergodicity, randomness, sensitivity to initial conditions and other characteristics and the ICPSO is used to make the chaos into the inertia weight factor parameters and The chaos is applied to the optimization of the RBF kernel function parameter g and the penalty factor C, and to improve the convergence speed and precision of the particle swarm optimization. The experimental results show that: relative to the PSO-SVM algorithm and GA-SVM algorithm, ICPSO-SVM improves the efficiency of intrusion detection, and is an effective intrusion detection model.