Network Intrusion Detection Based on the Integration of SVM and Genetic Algorithm
Zhang Fe · Journal of Qingdao University of Science and Technology · 2013
The purpose of this paper is to do research on the problems of network intrusion detection.Integration of the SVM and GA algorithms are applied to the intrusion detection field to distinct the normal and abnormal user behaviors,for network intrusion detection system.Traditional SVM algorithm is easy to produce the inappropriate choice of training parameters,which is difficult to obtain high detection efficiency and classification accuracy.In response to these problems,we propose a SVM-GA based optimization of the integration of intrusion detection,which firstly normalizes the network intrusion data to simplify the input,then optimizes SVM training parameters simultaneously with the genetic algorithm,and finally detects network intrusion making the use of SVM algorithm to achieve the classification and reorganization results.Simulation results show that the fusion algorithm have shorter training time,higher accuracy,lower false positive and false negative rate,which is a feasible and effective network intrusion detection method.