电力系统保护与控制2024，Vol.52Issue(14)：46-58,13.DOI:10.19783/j.cnki.pspc.240042
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
Location detection of a false data injection attack in a cyber-physical power system based on a hybrid chimp optimized extreme learning machine
摘要
Abstract
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.关键词
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Key words
cyber-physical power system/false data injection attack/extreme learning machine/chimp optimization引用本文复制引用
席磊,董璐,程琛,田习龙,李宗泽..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[J].电力系统保护与控制,2024,52(14):46-58,13.基金项目
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) （No.52277108）
