Defense of Massive False Data Injection Attack via Sparse Attack Points Considering Uncertain Topological Changes

作     者：Xiaoge Huang Zhijun Qin Ming Xie Hui Liu Liang Meng Xiaoge Huang;Zhijun Qin;Ming Xie;Hui Liu;Liang Meng

作者机构：School of Electrical EngineeringGuangxi UniversityNanningChina Guangxi Power Grid Co.Ltd.China Southern Grid(CSG)GuilinChina 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2022年第10卷第6期

页      面：1588-1598页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 0839[工学-网络空间安全] 08[工学] 

基　　金：supported in part by the National Natural Science Foundation of China(No.51767001) 

主　　题：False data injection attack auto-encoder generative adversarial network state estimation cyber security 

摘      要：False data injection attack(FDIA)is a typical cyber-attack aiming at falsifying measurement data for state estimation(SE),which may incur catastrophic consequences on cyber-physical system *** this paper,we develop a deep learning based methodology for detection,localization,and data recovery of FDIA on power systems in a coherent and holistic ***,the multi-modal probability distributions of both measurements and state variables in SE due to ever-changing operating points and structural/topological changes pose great challenges in detecting and localizing *** address this challenge,we first propose an enhanced attack model to launch massive FDIA on limited access ***,we train an auto-encoder(AE)with a Bayesian change verification(BCV)classifier using N-1 contingencies to detect FDIA with unseen N-k operational ***,to avoid model collapse caused by multi-modal measurement distribution,an AE-based generative adversarial network(GAN)is derived to generate a diverse candidate set of normal measurement vectors with various operational ***,we develop a pattern match algorithm to localize and recover the falsified measurements and state variables by comparing the falsified measurement vectors with the normal measurement vectors in the candidate *** studies with IEEE benchmark systems and a modified 415-bus China Southern Grid system are provided to validate the proposed *** shows that the proposed methodology achieves an average 95%accuracy for detection,over 80%accuracy for localization of FDIA,and recovers the measurement and state variables close to their true values.