基于混合黑猩猩优化极限学习机的电力信息物理系统虚假数据注入攻击定位检测OA北大核心CSTPCD
Location detection of a false data injection attack in a cyber-physical power system based on a hybrid chimp optimized extreme learning machine
针对已有检测方法无法对虚假数据注入攻击(false data injection attack,FDIA)进行精确定位的问题,提出了一种基于混合黑猩猩优化极限学习机(extreme learning machine,ELM)的电力信息物理系统FDIA的定位检测方法.首先,使用 ELM 作为分类器,用于提取电力数据特征并检测系统各节点的异常状态.然后,采用一种具有全局搜索能力且局部收敛速度更快的混合黑猩猩优化策略,用于寻找 ELM 最优隐藏层神经元数量.建立基于混合黑猩猩优化ELM的检测方法,实现对FDIA的精准定位,有利于后续防御措施的实施.最后,在IEEE 14和IEEE 57节点系统中进行大量仿真对比实验.结果表明,所提方法具有更佳的准确率、查准率、查全率和F1值,对FDIA能够进行更为精准的定位检测.
Existing detection methods cannot accurately locate a false data injection attack(FDIA).Thus a location detection method based on a hybrid chimp optimized extreme learning machine(ELM)is proposed for FDIA in a cyber-physical power system.First,an ELM is used as a classifier to extract the features of power data and detect the attacked state of each bus in the system.Then,a hybrid chimp optimization with global search and faster speed of local convergence is adopted to optimize the number of hidden layer neurons of the ELM.Thus,a detection method is established to realize the accurate location detection against FDIA.This is conducive to the implementation of subsequent defense measures.Finally,a large number of simulation experiments are carried out in IEEE14 and IEEE57 bus systems.The results show that the proposed method has better accuracy,precision,recall and F1 score.This means this method can carry out more accurate location detection against FDIA.
席磊;董璐;程琛;田习龙;李宗泽
梯级水电站运行与控制湖北省重点实验室,湖北 宜昌 443002||三峡大学电气与新能源学院,湖北 宜昌 443002三峡大学电气与新能源学院,湖北 宜昌 443002
电力信息物理系统虚假数据注入攻击极限学习机黑猩猩优化
cyber-physical power systemfalse data injection attackextreme learning machinechimp optimization
《电力系统保护与控制》 2024 (014)
46-58 / 13
This work is supported by the National Natural Science Foundation of China(No.52277108). 国家自然科学基金项目资助(52277108)
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