Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

A Robust Learning Framework for Smart Grids in Defense Against False-Data Injection Attacks

Published: 09 January 2024 Publication History

Abstract

With the rapid development of the application of smart grids in different sectors, security management has become a major concern due to cyber attack risks. Correctly and accurately estimating the real status of a smart grid under false-data injection attacks (FDIAs) is currently an emerging concern. In response to that concern, this work proposes a distributed robust learning framework for the inference of the working status under data integrity attacks. The proposed paradigm incorporates the technology median-of-means that enables identifying the correct state against various kinds of FDIAs that can efficiently prevent misleading information during the decision-making process in control centers. Compared with existing defense methods, our method is entirely data driven without training data, highly accurate, and reliable for wide-spectrum FDIAs. More important, it is capable of defending large-scale power electronic networks due to its distributed learning framework. Extensive experimental results demonstrate that our approach can provide efficient protection for Photovoltaic (PV) systems from FDIAs.

References

[1]
Liang Zhao, Jiaming Li, Qi Li, and Fangyu Li. 2022. A federated learning framework for detecting false data injection attacks in solar farms. IEEE Transactions on Power Electronics 37, 3 (2022), 2496–2501.
[2]
Fangyu Li, Qi Li, Jinan Zhang, Jiabao Kou, Jin Ye, WenZhan Song, and Homer Alan Mantooth. 2021. Detection and diagnosis of data integrity attacks in solar farms based on multilayer long short-term memory network. IEEE Transactions on Power Electronics 36, 3 (2021), 2495–2498.
[3]
Juan Carlos Balda, Alan Mantooth, Rick Blum, and Paolo Tenti. 2017. Cybersecurity and power electronics: Addressing the security vulnerabilities of the Internet of Things. IEEE Power Electronics Magazine 4 (2017), 37–43.
[4]
Fangyu Li, Aditya Shinde, Yang Shi, Jin Ye, Xiang-Yang Li, and Wenzhan Song. 2019. System statistics learning-based IoT security: Feasibility and suitability. IEEE Internet of Things Journal 6, 4 (2019), 6396–6403.
[5]
Fangyu Li, Rui Xie, Bowen Yang, Lulu Guo, Ping Ma, Jianjun Shi, Jin Ye, and WenZhan Song. 2022. Detection and identification of cyber and physical attacks on distribution power grids with PVs: An online high-dimensional data-driven approach. IEEE Journal of Emerging and Selected Topics in Power Electronics 10, 1 (2022), 1282–1291.
[6]
Jun Yu, Huimin Cheng, Jinan Zhang, Qi Li, Shushan Wu, Wenxuan Zhong, Jin Ye, Wenzhan Song, and Ping Ma. 2022. CONGO\(^2\): Scalable online anomaly detection and localization in power electronics networks. IEEE Internet of Things Journal 9, 15 (2022), 13862–13875.
[7]
Bowen Yang, Lulu Guo, Fangyu Li, Jin Ye, and Wenzhan Song. 2020. Vulnerability assessments of electric drive systems due to sensor data integrity attacks. IEEE Transactions on Industrial Informatics 16, 5 (2020), 3301–3310.
[8]
Kaishun Xiahou, Yang Liu, and Q. H. Wu. 2022. Decentralized detection and mitigation of multiple false data injection attacks in multiarea power systems. IEEE Journal of Emerging and Selected Topics in Industrial Electronics 3, 1 (2022), 101–112.
[9]
Beibei Li, Rongxing Lu, Gaoxi Xiao, Tao Li, and Kim-Kwang Raymond Choo. 2022. Detection of false data injection attacks on smart grids: A resilience-enhanced scheme. IEEE Transactions on Power Systems 37, 4 (2022), 2679–2692.
[10]
Qi Wang, Wei Tai, Yi Tang, and Ming Ni. 2019. Review of the false data injection attack against the cyber-physical power system. IET Cyber-Physical Systems: Theory & Applications 4, 2 (2019), 101–107.
[11]
Magdi S. Mahmoud and Yuanqing Xia. 2020. False data injection attacks. In Cloud Control Systems, Stephen Ison and Lucy Budd (Eds.). Academic Press, 149–167.
[12]
Brent Kesler. 2011. The Vulnerability of Nuclear Facilities to Cyber Attack.Strategic Insights Spring 10, 1 (2011), 15–25.
[13]
L. Mili, Th. Van Cutsem, and M. Ribbens-Pavella. 1985. Bad data identification methods in power system state estimation — a comparative study. IEEE Power Engineering Review PER-5, 11 (1985), 27–28.
[14]
Jeu-Min Lin and Heng-Yau Pan. 2007. A static state estimation approach including bad data detection and identification in power systems. In 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, 1–7.
[15]
Youbiao He, Gihan J. Mendis, and Jin Wei. 2017. Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism. IEEE Transactions on Smart Grid 8, 5 (2017), 2505–2516.
[16]
Saeed Ahmed, Youngdoo Lee, Seung-Ho Hyun, and Insoo Koo. 2019. Unsupervised machine learning-based detection of covert data integrity assault in smart grid networks utilizing Isolation Forest. IEEE Transactions on Information Forensics and Security 14, 10 (2019), 2765–2777.
[17]
Yufeng Wang, Zhihao Zhang, Jianhua Ma, and Qun Jin. 2022. KFRNN: An effective false data injection attack detection in smart grid based on Kalman filter and recurrent neural network. IEEE Internet of Things Journal 9, 9 (2022), 6893–6904.
[18]
Thusitha Dayaratne, Mahsa Salehi, Carsten Rudolph, and Ariel Liebman. 2022. False data injection attack detection for secure distributed demand response in smart grids. In 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN’22). Baltimore, MD, 367–380.
[19]
L. Mili, M. G. Cheniae, N. S. Vichare, and P. J. Rousseeuw. 1992. Algorithms for least median of squares state estimation of power systems. In Proceedings of the 35th Midwest Symposium on Circuits and Systems. Washington, DC, 1276–1283.
[20]
R. A. Jabr. 2005. Power system Huber M-estimation with equality and inequality constraints. Electric Power Systems Research 74, 2 (2005), 239–246.
[21]
Yacine Chakhchoukh and Hideaki Ishii. 2015. Coordinated cyber-attacks on the measurement function in hybrid state estimation. IEEE Transactions on Power Systems 30, 5 (2015), 2487–2497.
[22]
Yacine Chakhchoukh and Hideaki Ishii. 2016. Enhancing robustness to cyber-attacks in power systems through multiple least trimmed squares state estimations. IEEE Transactions on Power Systems 31, 6 (2016), 4395–4405.
[23]
Naime Ahmadi, Yacine Chakhchoukh, and Hideaki Ishii. 2021. Power systems decomposition for robustifying state estimation under cyber attacks. IEEE Transactions on Power Systems 36, 3 (2021), 1922–1933.
[24]
Yang Weng, Rohit Negi, Qixing Liu, and Marija D. Ilić. 2011. Robust state-estimation procedure using a least trimmed squares pre-processor. In ISGT’11. Anaheim, CA, 1–6.
[25]
Ibrahim Omar Habiballah and Yuanhai Xia. 2020. Least trimmed absolute value state estimator. In Advances in Electric Power and Energy: Static State Estimation. 255–294.
[26]
M. Blum, R. W. Floyd, Vaughan Pratt, R. L. Rivest, and Robert E. Tarjan. 1973. Time bounds for selection. Journal of Computer and System Sciences 7, 4 (1973), 448–461.
[27]
Fabian Kuhn, Thomas Locher, and Rogert Wattenhofer. 2007. Tight bounds for distributed selection. In Proceedings of the 19th Annual ACM Symposium on Parallel Algorithms and Architectures (San Diego, CA) (SPAA’07). ACM, New York, NY, 145–153.
[28]
Maria Valero, Fangyu Li, Sili Wang, Fan-Chi Lin, and WenZhan Song. 2019. Real-time cooperative analytics for ambient noise tomography in sensor networks. IEEE Transactions on Signal and Information Processing over Networks 5, 2 (2019), 375–389.
[29]
Xifan Wang, Wanliang Fang, and Zhengchun Du. 2003. Modern Power System Analysis (in Chinese). Science Press.
[30]
Zonghan Yu and Wenlong Chin. 2015. Blind false data injection attack using PCA approximation method in smart grid. IEEE Transactions on Smart Grid 6, 3 (2015), 1219–1226.
[31]
Ray Daniel Zimmerman, Carlos Edmundo Murillo-Sánchez, and Robert John Thomas. 2011. MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on Power Systems 26, 1 (2011), 12–19.
[32]
R. A. Jabr. 2005. Power system Huber M-estimation with equality and inequality constraints. Electric Power Systems Research 74, 2 (2005), 239–246.
[33]
Chaojun Gu, Jirutitijaroen Panida, and Motani Mehul. 2015. Detecting false data injection attacks in AC state estimation. IEEE Transactions on Smart Grid 6, 5 (2015), 2476–2483.
[34]
B. L. S. Prakasa Rao. 1984. The rate of convergence of the least squares estimator in a non-linear regression model with dependent errors. Journal of Multivariate Analysis 14, 3 (1984), 315–322.

Index Terms

  1. A Robust Learning Framework for Smart Grids in Defense Against False-Data Injection Attacks

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Sensor Networks
    ACM Transactions on Sensor Networks  Volume 20, Issue 2
    March 2024
    572 pages
    EISSN:1550-4867
    DOI:10.1145/3618080
    • Editor:
    • Wen Hu
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Journal Family

    Publication History

    Published: 09 January 2024
    Online AM: 20 March 2023
    Accepted: 09 March 2023
    Revised: 14 January 2023
    Received: 30 August 2022
    Published in TOSN Volume 20, Issue 2

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. False-data injection attacks
    2. distributional robust learning
    3. median-of-means
    4. smart grids

    Qualifiers

    • Research-article

    Funding Sources

    • NSFC
    • Beijing Municipal Natural Science Foundation
    • Beijing Institute of Technology research fund program for young scholars

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 247
      Total Downloads
    • Downloads (Last 12 months)163
    • Downloads (Last 6 weeks)20
    Reflects downloads up to 10 Nov 2024

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    Full Text

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media