Network Security Incidents Frequency Prediction Based on Improved Genetic Algorithm and LSSVM  

作　　者：ZHAO Guangyao ZOU Peng HAN Weihong 

机构地区：[1]School of Computer Science, National University of Defense Technology, Changsha, 410073, China [2]College of Equipment Command ＆Technology, Beijing, 100029, China

出　　处：《China Communications》2010年第4期126-131,共6页中国通信（英文版）

基　　金：supported in part by the National High Technology Research and Development Program of China ("863" Program) (No.2007AA010502)

摘　　要：Since the frequency of network security incidents is nonlinear,traditional prediction methods such as ARMA,Gray systems are difficult to deal with the problem.When the size of sample is small,methods based on artificial neural network may not reach a high degree of preciseness.Least Squares Support Vector Machines (LSSVM) is a kind of machine learning methods based on the statistics learning theory,it can be applied to solve small sample and non-linear problems very well.This paper applied LSSVM to predict the occur frequency of network security incidents.To improve the accuracy,it used an improved genetic algorithm to optimize the parameters of LSSVM.Verified by real data sets,the improved genetic algorithm (IGA) converges faster than the simple genetic algorithm (SGA),and has a higher efficiency in the optimization procedure.Specially,the optimized LSSVM model worked very well on the prediction of frequency of network security incidents.Since the frequency of network security incidents is nonlinear, traditional prediction methods such as ARMA, Gray systems are difficult to deal with the problem. When the size of sample is small, methods based on artificial neural network may not reach a high degree of preciseness. Least Squares Support Vector Machines （LSSVM） is a kind of machine learning methods based on the statistics learning theory, it can be applied to solve small sample and non-linear problems very well. This paper applied LSSVM to predict the occur frequency of network security incidents. To improve the accuracy, it used an improved genetic algorithm to optimize the parameters of LSSVM. Verified by real data sets, the improved genetic algorithm （IGA） converges faster than the simple genetic algorithm （SGA）, and has a higher efficiency in the optimization procedure. Specially, the optimized LSSVM model worked very well on the prediction of frequency of network security incidents.

关 键 词：Genetic Algorithm LSSVM Network Security Incidents Time Series PREDICTION 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TN91[自动化与计算机技术—计算机科学与技术]