基于增强型概率神经网络的安全态势要素获取

态势要素获取作为整个网络安全态势感知的基础,其质量的好坏将直接影响态势感知系统的性能。针对态势要素不易获取问题,提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化获取框架中,利用主成分分析(PCA)对训练样本属性进行约简并对特殊属性编码融合处理,将其结果用于优化概率神经网络(PNN)结构,降低系统复杂度。以PNN作为基分类器,基分类器通过反复迭代、权重更替,然后加权融合处理形成最终的强多分类器。实验结果表明,该方案是有效的态势要素获取方法并且精确度达到95.53%,明显优于同类算法,有较好的泛化能力。

Security situation element acquisition based on enhanced probabilistic neural network

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 complexity.PNN is taken as the base classifier to form the final strong classifier by repeated iteration,weight replacement and weighted fusion.The experimental results show that the scheme is an effective method to obtain the situation factors and its accuracy is 95.53%,significantly better than other similar algorithms.