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

作     者：ZHAO Guangyao ZOU Peng HAN Weihong ZHAO Guangyao;ZOU Peng;HAN Weihong

作者机构：School of Computer Science National University of Defense Technology Changsha 410073 China College of Equipment Command ＆Technology Beijing 100029 China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2010年第7卷第4期

页      面：126-131页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081001[工学-通信与信息系统] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：Genetic Algorithm LSSVM Network Security Incidents Time Series Prediction 

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 *** the size of sample is small,methods based on artificial neural network may not reach a high degree of *** 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 *** paper applied LSSVM to predict the occur frequency of network security *** improve the accuracy,it used an improved genetic algorithm to optimize the parameters of *** 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 ***,the optimized LSSVM model worked very well on the prediction of frequency of network security incidents.