A False Data Injection Attack Detecting and Compensating Method

doi:10.3969/j.issn.1671-1122.2023.06.003

Abstract

Abstract:

To accurately detect false data injection attacks in industrial control networks and quickly compensate for their impact on the system, this paper proposed an attack detecting and compensating method based on state estimation. The method constructed a sequence Kalman filter to optimally estimate the state vector based on the mathematical model of the industrial control system. Additionally, a double-judgment mechanism was designed to eliminate unstable states caused by noise and perturbation. Furthermore, the paper proposed a multi-step estimating attack compensation strategy that utilized the previously measured data in the safe state to provide a compensation control signal for the system. The experimental results conducted on the load frequency control system of the dual-area interconnected power system demonstrate the effectiveness of the proposed method in detecting and compensating for false data injection attacks. Moreover, the method outperforms the comparison algorithms in terms of frequency deviation control and control signal compensation.

Key words: industrial control system, false data injection attack, sequence Kalman filter, optimal state estimation, multi-step estimation

CLC Number:

TP309

XIE Ying, ZENG Zhu, HU Wei, DING Xuyang. A False Data Injection Attack Detecting and Compensating Method[J]. Netinfo Security, 2023, 23(6): 22-33.

Figures/Tables 13

References 21

[1]	DING Derui, HAN Qinglong, XIANG Yang, et al. A Survey on Security Control and Attack Detection for Industrial Cyber-Physical Systems[J]. Neurocomputing, 2018, 275: 1674-1683. doi: 10.1016/j.neucom.2017.10.009 URL
[2]	MUSLEH A S, GUO Chen, ZHAO Yangdong, et al. Attack Detection in Automatic Generation Control Systems Using LSTM-Based Stacked Autoencoders[J]. IEEE Transactions on Industrial Informatics, 2022, 19(1): 153-165. doi: 10.1109/TII.2022.3178418 URL
[3]	KRAVCHIK M, SHABTAI A. Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks[C]// ACM. The 2018 Workshop on Cyber-Physical Systems Security and Privacy. New York: ACM, 2018: 72-83.
[4]	YAN Jun, TANG Bo, HE Haibo. Detection of False Data Attacks in Smart Grid with Supervised Learning[C]// IEEE. 2016 International Joint Conference on Neural Networks(IJCNN). New York:IEEE, 2016: 1395-1402.
[5]	ASHRAFUZZAMAN M, DAS S, CHAKHCHOUKH Y, et al. Detecting Stealthy False Data Injection Attacks in the Smart Grid Using Ensemble-Based Machine Learning[EB/OL]. (2020-7-29)[2022-12-17]. https://doi.org/10.1016/j.cose.2020.101994. doi: https://doi.org/10.1016/j.cose.2020.101994
[6]	WAGHMARE S, KAZI F, SINGH N. Data Driven Approach to Attack Detection in a Cyber-Physical Smart Grid System[C]// IEEE. 2017 Indian Control Conference(ICC). New York:IEEE, 2017: 271-276.
[7]	WANG Yufeng, ZHANG Zhihao, MA Jianhua, et al. KFRNN: An Effective False Data Injection Attack Detection in Smart Grid Based on Kalman Filter and Recurrent Neural Network[J]. IEEE Internet of Things Journal, 2021, 9(9): 6893-6904. doi: 10.1109/JIOT.2021.3113900 URL
[8]	WANG Jiexin, LAI Yingxu, LIU Jing. Stealthy Attack Detection Method Based on Multi-Feature Long Short-Term Memory Prediction Model[J]. Future Generation Computer Systems, 2022, 137: 248-259. doi: 10.1016/j.future.2022.07.014 URL
[9]	MANANDHAR K, CAO Xiaojun, HU Fei, et al. Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter[J]. IEEE Transactions on Control of Network Systems, 2014, 1(4): 370-379. doi: 10.1109/TCNS.2014.2357531 URL
[10]	AMELI A, HOOSHYAR A, YAZDAVAR A H, et al. Attack Detection for Load Frequency Control Systems Using Stochastic Unknown Input Estimators[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(10): 2575-2590. doi: 10.1109/TIFS.2018.2824253 URL
[11]	KHALAF M, YOUSSEF A, EL-SAADANY E. A Particle Filter-Based Approach for the Detection of False Data Injection Attacks on Automatic Generation Control Systems[C]// IEEE. 2018 IEEE Electrical Power and Energy Conference(EPEC). New York:IEEE, 2018: 1-6.
[12]	AMELI A, HOOSHYAR A, EL-SAADANY E F, et al. Attack Detection and Identification for Automatic Generation Control Systems[J]. IEEE Transactions on Power Systems, 2018, 33(5): 4760-4774. doi: 10.1109/TPWRS.59 URL
[13]	YANG Qingyu, YANG Jie, YU Wei, et al. On False Data-Injection Attacks Against Power System State Estimation: Modeling and Countermeasures[J]. IEEE Transactions on Parallel and Distributed Systems, 2013, 25(3): 717-729. doi: 10.1109/TPDS.2013.92 URL
[14]	SHI Hanzhang, XIE Linbo, WU Zhihai, et al. Detection of False Data Injection Attacks in Power Grid Based on Coding Schemes[J]. Information and Control, 2021, 50(4): 419-426. doi: 10.13976/j.cnki.xk.2021.0398
	史晗璋, 谢林柏, 吴治海, 等. 基于编码策略的电网假数据注入攻击检测[J]. 信息与控制, 2021, 50(4): 419-426. doi: 10.13976/j.cnki.xk.2021.0398
[15]	LI Yuancheng, HUANG Rong, MA Longqiang. False Data Injection Attack and Defense Method on Load Frequency Control[J]. IEEE Internet of Things Journal, 2020, 8(4): 2910-2919. doi: 10.1109/JIoT.6488907 URL
[16]	ABBASPOUR A, SARGOLZAEI A, FOROUZANNEZHAD P, et al. Resilient Control Design for Load Frequency Control System Under False Data Injection Attacks[J]. IEEE Transactions on Industrial Electronics, 2019, 67(9): 7951-7962. doi: 10.1109/TIE.41 URL
[17]	BI Wenjun, ZHANG Kaifeng, YUAN Kun, et al. Observer-Based Attack Detection and Mitigation for Load Frequency Control System[C]// IEEE. 2019 IEEE Power & Energy Society General Meeting(PESGM). New York:IEEE, 2019: 1-5.
[18]	GU Yapei, YU Xiang, GUO Kexin, et al. Detection, Estimation, and Compensation of False Data Injection Attack for UAVs[J]. Information Sciences, 2021, 546: 723-741. doi: 10.1016/j.ins.2020.08.055 URL
[19]	HAN Jinglin, ZHAO Hui, HU Ping, et al. False Data Detection Strategy for Unified Control of Hybrid Microgrid[EB/OL]. (2022-12-14)[2022-12-17]. https://doi.org/10.19635/j.cnki.csu-epsa.001165. doi: https://doi.org/10.19635/j.cnki.csu-epsa.001165
	韩璟琳, 赵辉, 胡平, 等. 交直流微电网互联统一控制虚假数据检测策略[EB/OL]. (2022-12-14)[2022-12-17]. https://doi.org/10.19635/j.cnki.csu-epsa.001165. doi: https://doi.org/10.19635/j.cnki.csu-epsa.001165
[20]	MOUSAVIAN S, VALENZUELA J, WANG Jianhui. Real-Time Data Reassurance in Electrical Power Systems Based on Artificial Neural Networks[J]. Electric Power Systems Research, 2013, 96: 285-295. doi: 10.1016/j.epsr.2012.11.015 URL
[21]	YANG Fan, CHEN Houjin, LI Jupeng, et al. Single Shot Multibox Detector with Kalman Filter for Online Pedestrian Detection in Video[J]. IEEE Access, 2019: 15478-15488.

参数	说明
?f_i	区域i的频率偏差
?P_tie_,_i	区域i的联络线交换功率偏差
?P_gi	区域i的调速器阀门位置增量
?P_mi	区域i的涡轮机功率增量
?P_Li	区域i的负载增量
?P_ci	区域i的控制输入信号增量
β_i	区域i的频率偏置系数
R_i	区域i的下垂系数
T_gi	区域i的调速器时间常数
T_ti	区域i的涡轮机时间常数
H_i	区域i的系统惯性常数
D_i	区域i的负荷阻尼系数

	ADC-SE方法	GFDC	传统卡尔曼滤波方法
针对区域1频率的FDI攻击	0.036	0.104	0.152
针对区域1联络线交换功率的FDI攻击	0.037	0.268	0.271