摘要
针对当前电力系统网络安全挖掘方法误检率和漏检率较高的问题,提出基于黑盒遗传算法的电力系统网络安全漏洞挖掘方法.对电力系统网络态势进行全方位感知,得出电力系统网络整体安全态势情况,引入黑盒遗传算法进行黑盒模糊测试,选择目标函数并生成测试参数,将优化样本传输至模糊测试模块,通过日志监控测试系统实时记录异常情况.当模糊测试达到预设目标时停止测试,并将电力系统网络安全漏洞报告输出.结果表明,所提算法误检率较低,证明了所提算法可靠性较强.
Aiming at the problem of high false and loss detection rates in current network security mining for power system,a network security vulnerability mining method for power system based on black box genetic algorithm was proposed.The network situation was monitored with the omnidirectional perception,and the overall network security situation of power system was obtained.A black box genetic algorithm was introduced for black box fuzzy tests,and the target function was selected to generate test parameters.In addition,the optimized samples were transmitted to a fuzzy test module,and the real-time abnormal situation was recorded through a log monitoring and testing system.The test was stopped when a fuzzy test reaches the preset target,and a network security vulnerability report for power system was output.The results show that the false rates of as-proposed algorithm are lower,indicating that the reliability of as-proposed algorithm is higher.
作者
王小虎
王超
李群
任天宇
WANG Xiao-hu;WANG Chao;LI Qun;REN Tian-yu(Electric Power Research Institute,State Grid Beijing Electric Power Company,Beijing 100075,China;College of Information Technology,North China University of Technology,Beijing 100000,China)
出处
《沈阳工业大学学报》
CAS
北大核心
2021年第5期500-504,共5页
Journal of Shenyang University of Technology
基金
国家自然科学基金项目(61873030).
关键词
黑盒遗传算法
电力系统
网络安全漏洞
挖掘方法
黑盒模糊测试
网络安全态势
优化样本
安全漏洞报告
black box genetic algorithm
power system
network security vulnerability
mining method
black box fuzzy test
network security situation
optimization sample
security vulnerability report
