网络安全态势感知新方法

针对网络安全态势感知问题,为了提高态势感知和预测过程的速度和精准度,提出一种基于神经网络的网络安全态势感知方法. 首先利用网络安全态势评估的指标体系来表征整个网络的安全状态,然后提出一种基于逆向传播(BP)神经网络的网络安全态势评估方法. 为解决态势要素与评估结果之间的不确定性及模糊性问题,提出一种基于RBF神经网络的网络安全态势预测方法,利用RBF神经网络找出网络态势值的非线性映射关系,采用自适应遗传算法对网络参数进行优化并感知网络安全态势,在真实网络环境下对提出的方法进行验证,实验结果证明该方法对网络安全态势感知是可行和有效的.

New Method of Network Security Situation Awareness

Aiming at the problems of security situation awareness about networks, a network security situation awareness method based on neural network is proposed. Firstly, an index system of network security situation evaluation was built and the network security situation was defined with four independent properties. Then, a back propagation (BP) neural network based on the method to estimate the security situation assessment of network was designed. To solve the problem of uncertainty and fuzziness between the situation factor and the evaluation result, a network security situation forecast method that can find non-linear mapping relationship among network situational values was given based on radial basis function (RBF) neural network, parameters of the network were optimized through adaptive genetic algorithm and the network security situation awareness was obtained. An experiment with a real network environment was performed, the experimental results prove that our method is feasible and effective to the network security situation awareness.