摘要
为了提高网络潜在危险趋势估计的准确性,提出一种灰色关联分析和支持向量机的网络潜在危险趋势估计模型(Grey-SVM)。选择初始网络潜在危险评估指标体系,采用灰色关联分析计算各指标权重,采用支持向量机对权重的数据进行学习,模型参数采用混沌粒子群进行优化,建立网络潜在危险趋势估计模型,并采用DARPA99数据集进行测试。测试结果表明,Grey-SVM可以对网络潜在危险趋势进行准确、客观评估,提高了网络系统的安全。
To improve the accuracy of estimation of network potentially dangerous trend, a network risk estimation model （Grey SVM） is presnted based on grey trend relational analysis and support vector machine. Firstly, the potential danger of the initial network evaluation index system is built, and grey relational analysis is used to calculate the weight of each index, and then the support vector machine is used to learn the weight data, the model parameters are optimized by using chaotic particle swarm , fi- nally the network of potentially dangerous trend estimate model is built , and the performance is tested using DARPA99 data. The results show that the proposed model can accurately, objectively assess the potential risk tendency of network and improve network system security.
出处
《计算机工程与设计》
CSCD
北大核心
2013年第9期3058-3062,共5页
Computer Engineering and Design
关键词
网络潜在危险
评估指标
灰色关联分析
粒子群优化算法
支持向量机
network potential risk
evaluation
grey relational analysis
particle swarm optimization＇~ support vector machine
