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

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 operation.In 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 manner.However,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 FDIA.To address this challenge,we first propose an enhanced attack model to launch massive FDIA on limited access points.Second,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 topologies.Third,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 topologies.Finally,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 set.Case studies with IEEE benchmark systems and a modified 415-bus China Southern Grid system are provided to validate the proposed methodology.It 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.