基于粗糙集神经网络的网络安全态势评估方法

为克服多源数据融合中存在信息高维、冗余和噪音等大量不确定性因素给网络安全态势评估带来的复杂影响,提出一种基于粗糙集神经网络的网络安全态势评估方法。该方法既利用粗糙集理论在机械学习、处理冗余信息和特征提取等方面的能力,又结合神经网络处理噪音和任意逼近能力构造出由指标层、离散层、规则层、决策层组成的态势评估模型,并与BP神经网络方法进行对比研究。仿真实验结果表明,所提方法偏差较少,更能客观、准确地分析网络安全状况。

Network Security Situation Evaluation Based on Rough Set and Neural Network

In order to overcome the complex influences of uncertain factors of information high dimension, redundancy and noise etc. in the multi-source data fusion on the network security situation evaluation, presents a network security situation assessment method based on rough set and neural network. This method uses rough set theory capabilities in machine learning, redundant information processing and feature extraction and combines with the neural network ability of dealing with noise and arbitrary approximation to construct the situation assessing model composed of index layer, discrete layer, rule layer and decision layer, and compares and studies it with BP neural network method. The simulation experiment shows that the method has less deviation and can analyze network security situation more objectively and accurately.