综合智慧能源2025，Vol.47Issue(11)：52-61,10.DOI:10.3969/j.issn.2097-0706.2025.11.005
基于多元检测模型的信息物理系统网络攻击防御机制
Multivariate detection model-based defense mechanism against cyber attacks on cyber-physical power systems
摘要
Abstract
False data injection attacks pose a severe threat that cannot be overlooked during the development of new cyber-physical power systems.These attacks can tamper with power grid data to create false grid states,mislead operators into making incorrect operational decisions,and consequently disrupt the stable operation of the power system.Moreover,existing defense methods are incapable of addressing attacks involving complex data types or pinpointing abnormal states.Therefore,a multi-scenario AC false data injection attack strategy was proposed,and an attack model better aligning with actual power grid environments and exhibiting strong stealthiness was constructed.On this basis,a defense mechanism based on a multivariate detection model was designed,effectively integrating the advantages of three detectors:extreme learning machine,extreme gradient boosting,and light gradient boosting machine.Using multi-scenario attack cases as training data,an efficient attack detection model capable of pinpointing abnormal states was formed.Both the attack and defense models were simulated in IEEE 14-bus and IEEE 57-bus systems.The experimental results verified the effectiveness,stealthiness,and diversity of the attacks,as well as the real-time performance and accuracy of the detection mechanism.关键词
信息物理系统/电力信息安全/虚假数据注入攻击/多元检测模型/极限学习机/极端梯度提升树/轻量级梯度提升器Key words
cyber-physical power system/electric power system information security/false data injection attack/multivariate detection model/extreme learning machine/extreme gradient boosting/light gradient boosting machine分类
能源科技引用本文复制引用
薛雯丽,洪晓燕,杨文杰,吴婷..基于多元检测模型的信息物理系统网络攻击防御机制[J].综合智慧能源,2025,47(11):52-61,10.基金项目
广东省基础与应用基础研究基金项目(2024A151 5011012)Basic and Applied Basic Research Foundation of Guangdong Province(2024A1515011012) （2024A151 5011012）
