摘要
电力信息网面临着日益严峻的网络攻击风险威胁,传统网络安全态势量化方法仅从网络性能角度进行分析,忽略了各种电力应用业务的重要性对安全态势的影响,导致量化结果难以全方位反映电力信息网络风险状态。文中提出一种基于进化神经网络的电力信息网安全态势量化方法,首先,通过分析电力通信网络应用业务特点,设计面向电力通信网的安全态势体系架构(PIN-NSSQ);其次,从网络可靠性、威胁性、脆弱性3个维度出发,结合电力业务重要性,建立耦合互联的空间要素指标体系,并实现关键要素指标的数学化表征;然后,将遗传进化算法优化的BP神经网络融入要素指标计算过程中,构建基于进化神经网络的电力信息网安全态势量化模型,有效实现对电力信息网络安全态势全面感知过程的高效计算及结果精确量化;最后,根据某电力部门网络拓扑搭建仿真实验环境,验证了所提方法的有效性和鲁棒性。
Electric power information networks are facing increasingly severe threats of cyber attacks.Traditional quantification methods of network security situation only analyze from the perspective of network performance,ignoring the impact of the importance of various power application services on the security situation,making it difficult for the quantitative results to fully reflect the power information Cyber risk status.This paper proposes a power information network security situation quantification method based on evolutionary neural networks.First,by analyzing the characteristics of power communication network applications,a power communication network-oriented security situation architecture(PIN-NSSQ)is designed.Secondly,Starting from the three dimensions ofnetwork reliability,threat and vulnerability,combined with the importance of power business,a coupled and interconnected spatial element index system is established,to realize the mathematical representation of key element indicators.Then,integrating the BP neural network optimized by genetic evolution algorithm into the calculation process of element indicators,a power information network security situation quantification model based on evolutionary neural network is constructed to effectively realize the efficient calculation and precise quantification of the process of comprehensive perception of the power information network security situation.Finally,a simulation experiment environment is built according to a certain power department network topology to verify The effectiveness and robustness of the method proposed in the article.
作者
吕鹏鹏
王少影
周文芳
连阳阳
高丽芳
LYU Peng-peng;WANG Shao-ying;ZHOU Wen-fang;LIAN Yang-yang;GAO Li-fang(Information&Telecommunications Branch,State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050000,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S01期588-593,共6页
Computer Science
基金
国网河北省电力有限公司科技项目(kj2019-042)。
关键词
电力信息网
网络安全
数学化表征
进化神经网络
态势感知
Power information network
Network security
Mathematical representation
Evolutionary neural network
Situational awareness
