Research On Network Intrusion Detection Based On ICPSO-SVM Method

Network intrusion detection is a hot topic in the research of network security,and intrusion detection technology based on artificial intelligence algorithm is an important research direction.Based on chaos particle swarm optimization algorithm combined with support vector machine network intrusion detection technology has strong nonlinear processing ability,able to identify a large number of unknown,a new network intrusion behavior,so as to improve the security of the network.However,the existing algorithm based on swarm intelligence algorithm has the shortcomings of high false alarm rate,long training time and poor ability to detect unknown intrusion data.In this thesis,the application of the theory and algorithm of chaos particle swarm optimization and support vector machine in intrusion detection is analyzed and studied in detail,the main research findings and innovation are as follows:An adaptive chaotic particle swarm optimization algorithm is proposed.The idea of ICPSO algorithm is to formulate a chaotic disturbance adjustment strategy for inertia weight by using the property of ergodicity,randomicity,and the sensitivity to initial conditions of chaos,which overcomes the problem of slow convergence and easy to fall into local optimum.Finally,select four typical test functions which are used to experiment and comparative analysis.It is proved that ICPSO algorithm compared with other algorithm has a faster convergence speed and higher precision.It also shows that the algorithm is effective in solving complex optimization problems such as high dimension and multi pole points.A new intrusion detection model based on adaptive chaotic particle swarm optimization support vector machine parameter algorithm is proposed.By analyzing the importance of parameters for SVM model,a ICPSO-SVM model based intrusion detection has been proposed.In this model,to begin with,the kernel parameters and penalty coefficients of support vector machine are optimized by using ICPSO algorithm,and then the optimal parameters are substituted into the SVM with mixed kernel,furthermore,SVM model machine learning through training sample,finally,an optimal SVM intrusion detection model is established.The experiments are designed to verify the effectiveness of the proposed ICPSO-SVM based on network intrusion detection model.Experimental results show that the ICPSO-SVM model has been greatly improved in the efficiency of intrusion detection and false alarm rate compared with the existing GA-SVM model and PSO-SVM model,which is an effective intrusion detection model.