基于遗传优化PNN神经网络的网络安全态势评估

Network Security Situation Assessment Based on Genetic Optimized PNN Neural Network

为提高网络安全态势评估的准确率,提出了一种基于遗传优化概率神经网络的网络安全态势评估。首先,在网络安全态势评估建模过程中,根据网络安全态势特点和常见评估等级建立了概率神经网络的网络安全态势评估模型,以便充分挖掘概率神经网络在网络安全态势评估细粒度方面的优势。然后,为了防止因网络安全态势参数细粒度评估而造成收敛速度过慢的情况发生,对概率神经网络的修正因子进行遗传算法优化,并再次进行概率神经网络训练,从而得到稳定的概率网络安全态势评估模型。经过实验证明,相比传统的概率神经网络算法,基于遗传算法优化概率神经网络的网络安全态势评估准确度更高,平均准确率达到90%以上,且训练速度更快。

In order to improve the performance of network security situation assessment,this paper presents a network security situation assessment method based on genetic optimization probabilistic neural network.Firstly,In the process of network security situation assessment modeling,according to the characteristics of network security situation and common evaluation levels,the network security situation assessment model of PNN neural network is established,and the advantages of PNN neural network in fine-grained network security situation assessment are fully exploited.Then,in order to prevent the slow convergence caused by the fine-grained evaluation of network security situation parameters,the correction factors of PNN are left,and then the stable PNN network security situation assessment model is obtained by iterative training of PNN neural network.Experiments show that compared with the traditional PNN neural network algorithm,by using genetic algorithm to optimize the PNN network security situation assessment classification,evaluation accuracy is higher,average accuracy rate is more than 90%,and training speed is faster.