电讯技术2017，Vol.57Issue(1)：64-71,8.DOI:10.3969/j.issn.1001-893x.2017.01.011
基于增强型概率神经网络的安全态势要素获取
Security Situation Element Acquisition Based on Enhanced Probabilistic Neural Network
摘要
Abstract
Situation elements extraction is the basis of the whole network security situation awareness and its quality will directly affect the performance of the situation awareness system. To solve the problem that the situation element is difficult to extract,a method is proposed to extract the hierarchical frame situation elements based on the enhanced probabilistic neural network(PNN). In the hierarchical access frame,the principal component analysis( PCA) is used to reduct the training sample attribute and process the special attribute encoding fusion. The result is used to optimize the structure of PNN and reduce the system com-plexity. PNN is taken as the base classifier to form the final strong classifier by repeated iteration,weight re-placement and weighted fusion. The experimental results show that the scheme is an effective method to ob-tain the situation factors and its accuracy is 95. 53%,significantly better than other similar algorithms.关键词
网络安全/态势要素/数据处理/协同增强/概率神经网络Key words
network security/situational factors/data handling/synergistic enhancement/probabilistic neu-ral network( PNN)分类
信息技术与安全科学引用本文复制引用
李方伟,王森,朱江,张海波..基于增强型概率神经网络的安全态势要素获取[J].电讯技术,2017,57(1):64-71,8.基金项目
国家自然科学基金资助项目(61271260) （61271260）
重庆市科委自然科学基金资助项目(cstc2015jcyjA40050) （cstc2015jcyjA40050）
重庆市教委科学技术研究项目(KJ120530) （KJ120530）
