Information Security Risk Assessment Model Based on Optimized Support Vector Machine with Artificial Fish Swarm Algorithm

被引：0

作者：

Gao, Yiyu ^{[1
]}

Shen, YongJun ^{[1
]}

Zhang, GuiDong ^{[1
]}

Zheng, Shang ^{[1
]}

机构：

[1] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Gansu, Peoples R China

来源：

PROCEEDINGS OF 2015 6TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE | 2015年

关键词：

AFSA; risk assessment; support vector machine; optimization (key words);

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Because the information security risk assessment have the problem of less training data and slow convergence rate, we put forward a information security risk assessment model based on support vector machine (SVM) using artificial fish swarm algorithm (AFSA). In this paper, we used weekly security report of the government network security situation from China National Internet Emergency Center(CNCERT) as the source data [1]. We adopted the RBF function as the kernel function SVM, then optimized the penalty coefficient C and kernel function parameter delta of artificial fish swarm algorithm. At the end of this paper, we established the optimal evaluation model for simulation. Our results showed that the information security risk assessment model based on AFSA_SVM has higher accuracy and faster convergence rate than the one of cross-validation. (Abstract)

引用

页码：599 / 602

页数：4