Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

Zhang, Ying; Wang, Jianhui; Chen, Bo

doi:10.1109/tsg.2020.3010510

Cited by 332Smart Grid Security and ResiliencePower System Optimization and StabilityElectricity Theft Detection Techniques

Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

Open access

Zhang, Ying, Wang, Jianhui, Chen, Bo·2021·IEEE Transactions on Smart Grid

DOI 10.1109/tsg.2020.3010510

Open this paper in CoChat — free account Open PDF

Abstract

The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1109/tsg.2020.3010510

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The prevalence of outliers in modern distribution systems poses a challenge to obtaining reliable power flow solutions.
“Besides, the prevalence of outliers in modern distribution systems poses another challenge to obtaining reliable PF solutions. Anomalous data, recorded by meters, might arise for a variety of reasons, due to meter malfunction, false data injection attacks, or communication interruption [12], [13].”
From Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method (2024) · cites this paper as [13]
The Introduction (page unavailable) says false data injection attacks can degrade state estimation in distribution systems, and “D. Impacts of Measurement Noises” discusses smart-meter malfunction. The paper does not establish that outliers are prevalent or that they impede reliable power-flow solutions; the passages found also do not identify communication interruption as a cause of anomalous meter data. (LLM, conf=0.83)
Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Generative Adversarial Nets
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
False data injection attacks against state estimation in electric power grids
Liu, Y., Ning, P., & Reiter, M. K. (2011) · ACM Transactions on Information and System Security · doi:10.1145/1952982.1952995 · Cited by 2,469 · see citing works
Adversarial Autoencoders
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015) · arXiv · doi:10.48550/arxiv.1511.05644 · Cited by 1,579 · see citing works
Distribution System Modeling and Analysis
Kersting, W. (2012) · Electric Power Engineering Series · doi:10.1201/b12056-31 · Cited by 1,241 · see citing works
Cyber–Physical System Security for the Electric Power Grid
Sridhar, S., Hahn, A., & Govindarasu, M. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2165269 · Cited by 1,219 · see citing works
A Review of False Data Injection Attacks Against Modern Power Systems
Liang, G., Zhao, J., Luo, F., Weller, S. R., & Dong, Z. Y. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2495133 · Cited by 1,033 · see citing works
Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism
He, Y., Mendis, G. J., & Wei, J. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2703842 · Cited by 844 · see citing works
Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter
Manandhar, K., Cao, X., Hu, F., & Liu, Y. (2014) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2014.2357531 · Cited by 791 · see citing works
Machine Learning Methods for Attack Detection in the Smart Grid
Özay, M., Esnaola, I., Vural, F. T. Y., Kulkarni, S. R., & Poor, H. V. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2404803 · Cited by 645 · see citing works
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
Dehghanpour, K., Wang, Z., Wang, J., Yuan, Y., & Bu, F. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2870600 · Cited by 491 · see citing works
Self-citation
On the Existence and Linear Approximation of the Power Flow Solution in Power Distribution Networks
Bolognani, S., & Zampieri, S. (2014) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2015.2395452 · Cited by 465 · see citing works
Detecting False Data Injection Attacks on Power Grid by Sparse Optimization
Liu, L., Esmalifalak, M., Ding, Q., Emesih, V. A., & Han, Z. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2284438 · Cited by 447 · see citing works
Detecting False Data Injection Attacks in AC State Estimation
Chaojun, G., Jirutitijaroen, P., & Motani, M. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2388545 · Cited by 420 · see citing works
Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks
Yu, J. J. Q., Hou, Y., & Li, V. O. K. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2825243 · Cited by 383 · see citing works
Graph-Based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays
Zhao, Y., Ball, R., Mosesian, J., de Palma, J.-F., & Lehman, B. (2014) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2014.2364203 · Cited by 338 · see citing works
False Data Attacks Against AC State Estimation With Incomplete Network Information
Liu, X., & Li, Z. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2521178 · Cited by 262 · see citing works
A Fully Data-Driven Method Based on Generative Adversarial Networks for Power System Dynamic Security Assessment With Missing Data
Ren, C., & Xu, Y. H. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2922671 · Cited by 248 · see citing works
False Data Injection Attacks Against State Estimation in Power Distribution Systems
Deng, R., Zhuang, P., & Liang, H. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2813280 · Cited by 232 · see citing works
Distribution System Modeling and Analysis
Kersting, W. H. (2006) · CRC Press eBooks · doi:10.1201/9781420006162 · Cited by 232 · see citing works
Interval Deep Generative Neural Network for Wind Speed Forecasting
Khodayar, M., Wang, J., & Manthouri, M. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2847223 · Cited by 197 · see citing works
Self-citation
Detection of false data injection attacks against state estimation in smart grids based on a mixture Gaussian distribution learning method
Foroutan, S. A., & Salmasi, F. R. (2017) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/iet-cps.2017.0013 · Cited by 148 · see citing works
Semisupervised Autoencoders for Speech Emotion Recognition
Deng, J., Xu, X.-Z., Zhang, Z.-X., Frühholz, S., & Schuller, B. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2759338 · Cited by 135 · see citing works
Deep Learning-Based Time-Varying Parameter Identification for System-Wide Load Modeling
Cui, M., Khodayar, M., Chen, C., Wang, X., Zhang, Y., & Khodayar, M. E. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2896493 · Cited by 113 · see citing works
Self-citation
False Data Injection Attacks Against State Estimation in Multiphase and Unbalanced Smart Distribution Systems
Zhuang, P., Deng, R., & Liang, H. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2895306 · Cited by 108 · see citing works
Scalable Semisupervised GMM for Big Data Quality Prediction in Multimode Processes
Yao, L., & Ge, Z. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2856200 · Cited by 87 · see citing works
Interval State Estimation With Uncertainty of Distributed Generation and Line Parameters in Unbalanced Distribution Systems
Zhang, Y., Wang, J.-H., & Li, Z.-S. (2020) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2926445 · Cited by 82 · see citing works
Self-citation
Uncertainty Modeling of Distributed Energy Resources: Techniques and Challenges
Zhang, Y., Wang, J., & Li, Z. (2019) · Current Sustainable/Renewable Energy Reports · doi:10.1007/s40518-019-00129-0 · Cited by 77 · see citing works
Self-citation
Wide-Area Monitoring of Power Systems Using Principal Component Analysis and $k$-Nearest Neighbor Analysis
Cai, L., Thornhill, N. F., Kuenzel, S., & Pal, B. C. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2783242 · Cited by 67 · see citing works
Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems
Zhang, Y., Wang, J., & Liu, J. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2937554 · Cited by 64 · see citing works
Self-citation
Probabilistic Deep Autoencoder for Power System Measurement Outlier Detection and Reconstruction
Lin, Y., & Wang, J. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2937043 · Cited by 59 · see citing works
Self-citation
Risk Assessment for Cyberattack in Active Distribution Systems Considering the Role of Feeder Automation
Dai, Q., Shi, L., & Ni, Y. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2899983 · Cited by 55 · see citing works
Memorization in Overparameterized AutoencodersPreprint
Radhakrishnan, A., Yang, K., Belkin, M., & Uhler, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10333 · Cited by 28 · see citing works

Flagged first, then by citations

1 of the 35 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

2017) IEEE test feeder specifications. [Online]. Available: http://sites.ieee.org/pes-testfeeders/resources
Dec. 2011) IEEE standard for synchrophasor measurements for power systems, IEEE Standard C37.118-2005.
Ying Zhang (S'18) received the M.S. degree in Electrical Engineering from Shandong University, Jinan, China, in 2017, and the Ph.D. degree in Department of Electrical and Computer Engineering from Southern Methodist University, Dallas, Texas, USA, in 2020. Her research interests include situa- tional awareness for power system monitoring and control via optimization and machine learning. She accepted the Frederick E. Terman Award in SMU. She serves as a reviewer in IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, and IEEE Power Engineering Letters, etc.
Dr. Wang has authored and/or co-authored more than 300 journal and conference publications, which have been cited for more than 20,000 times by his peers with an H-index of 72. He has been invited to give tutorials and keynote speeches at major conferences, including IEEE ISGT, IEEE SmartGridComm, IEEE SEGE, IEEE HPSC, and IGEC-XI. Dr. Wang is the past Editor-in-Chief of the IEEE Transactions on Smart Grid and an IEEE PES Distinguished Lecturer. He is also a guest editor of Proceedings of the IEEE special issue on power grid resilience. He is the recipient of the IEEE PES Power System Operation Committee Prize Paper Award in 2015 and the 2018 Premium Award for Best Paper in IET Cyber- Physical Systems: Theory & Applications.

Sorted by citations

Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future
Ghiasi, M. I., Niknam, T., Wang, Z., Mehrandezh, M., Dehghani, M., & Ghadimi, N. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108975 · Cited by 497 · see citing works
A Review on Digital Twin Technology in Smart Grid, Transportation System and Smart City: Challenges and Future
Jafari, M., Kavousi‐Fard, A., Chen, T., & Karimi, M. (2023) · IEEE Access · doi:10.1109/access.2023.3241588 · Cited by 428 · see citing works
Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach
Li, Y., Wei, X., Li, Y., Dong, Z. Y., & Shahidehpour, M. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3204796 · Cited by 414 · see citing works
Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges
Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2021.100452 · Cited by 291 · see citing works
A Review on Application of Artificial Intelligence Techniques in Microgrids
Mohammadi, E., Alizadeh, M., Asgarimoghaddam, M., Wang, X., & Simões, M. G. (2022) · IEEE Journal of Emerging and Selected Topics in Industrial Electronics · doi:10.1109/jestie.2022.3198504 · Cited by 191 · see citing works
A Review on Cybersecurity Analysis, Attack Detection, and Attack Defense Methods in Cyber-physical Power Systems
Du, D., Zhu, M., Li, X., Fei, M., Bu, S., Wu, L., & Li, K. (2023) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2021.000604 · Cited by 190 · see citing works
A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
Macas, M., Wu, C., & Fuertes, W. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109032 · Cited by 192 · see citing works
Smart Grid Cyber-Physical Situational Awareness of Complex Operational Technology Attacks: A Review
Nafees, M. N., Saxena, N., Cárdenas, Á. A., Grijalva, S., & Burnap, P. (2022) · ACM Computing Surveys · doi:10.1145/3565570 · Cited by 176 · see citing works
Applications of deep learning for phishing detection: a systematic literature review
Catal, C., Giray, G., Teki̇nerdoğan, B., Kumar, S., & Shukla, S. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01672-x · Cited by 155 · see citing works
Cyber-Attack Detection and Cyber-Security Enhancement in Smart DC-Microgrid Based on Blockchain Technology and Hilbert Huang Transform
Ghiasi, M. I., Dehghani, M., Niknam, T., Kavousi‐Fard, A., Siano, P., & Alhelou, H. H. (2021) · IEEE Access · doi:10.1109/access.2021.3059042 · Cited by 144 · see citing works
Synthetic attack data generation model applying generative adversarial network for intrusion detection
Kumar, V., & Sinha, D. (2022) · Computers & Security · doi:10.1016/j.cose.2022.103054 · Cited by 138 · see citing works
Study of smart grid cyber-security, examining architectures, communication networks, cyber-attacks, countermeasure techniques, and challenges
Achaal, B., Adda, M., Berger, M., Ibrahim, H., & Awde, A. (2024) · Cybersecurity · doi:10.1186/s42400-023-00200-w · Cited by 127 · see citing works
A review on machine learning techniques for secured cyber-physical systems in smart grid networks
Hasan, M. K., Abdulkadir, R. A., Islam, S., Gadekallu, T. R., & Safie, N. (2024) · Energy Reports · doi:10.1016/j.egyr.2023.12.040 · Cited by 104 · see citing works
Securing Cloud-Based Internet of Things: Challenges and Mitigations
Singh, N., Buyya, R., & Kim, H. (2024) · Sensors · doi:10.3390/s25010079 · Cited by 98 · see citing works
Cyber-Resilient Smart Cities: Detection of Malicious Attacks in Smart Grids
Mohammadpourfard, M., Khalili, A., Genc, I. V. M., & Konstantinou, C. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103116 · Cited by 97 · see citing works
Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids
Farajzadeh-Zanjani, M., Hallaji, E., Razavi‐Far, R., Saif, M., & Parvania, M. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3061395 · Cited by 96 · see citing works
Generalized Graph Neural Network-Based Detection of False Data Injection Attacks in Smart Grids
Takiddin, A., Atat, R., Ismail, M., Boyaci, O., Davis, K., & Serpedin, E. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3232821 · Cited by 95 · see citing works
Adversarial Attack Mitigation Strategy for Machine Learning-Based Network Attack Detection Model in Power System
Huang, R., & Li, Y. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3217060 · Cited by 94 · see citing works
A Smart Digital Twin Enabled Security Framework for Vehicle-to-Grid Cyber-Physical Systems
Ali, M., Kaddoum, G., Li, W.-T., Yuen, C., Tariq, M., & Poor, H. V. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3305916 · Cited by 93 · see citing works
Deep learning for cybersecurity in smart grids: Review and perspectives
Ruan, J., Liang, G., Zhao, J., Zhao, H. Q., Qiu, J., Wen, F., & Dong, Z. Y. (2023) · Energy Conversion and Economics · doi:10.1049/enc2.12091 · Cited by 91 · see citing works
A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence
Burgos, M. F. G., Morato, J., & Vizcaíno-Imacaña, P. (2024) · Applied Sciences · doi:10.3390/app14031194 · Cited by 91 · see citing works
A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning
Ortega-Fernandez, I., & Liberati, F. (2023) · Energies · doi:10.3390/en16020635 · Cited by 89 · see citing works
Edge AI for Internet of Energy: Challenges and perspectives
Himeur, Y., Sayed, A. N., Alsalemi, A., Bensaali, F., & Amira, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.101035 · Cited by 99 · see citing works
Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey
Le, M., Huynh‐The, T., Do‐Duy, T., Vu, T.-H., Hwang, W., & Pham, Q. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3427324 · Cited by 80 · see citing works
Localizing False Data Injection Attacks in Smart Grid: A Spectrum-Based Neural Network Approach
Peng, S., Zhang, Z., Deng, R., & Cheng, P. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3261970 · Cited by 74 · see citing works
Privacy-Preserved Generative Network for Trustworthy Anomaly Detection in Smart Grids: A Federated Semisupervised Approach
Abdel‐Basset, M., Moustafa, N., & Hawash, H. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3165869 · Cited by 73 · see citing works
Cyber-Security of Smart Grids: Attacks, Detection, Countermeasure Techniques, and Future Directions
Khoei, T. T., Slimane, H. O., & Kaabouch, N. (2022) · Communications and Network · doi:10.4236/cn.2022.144009 · Cited by 96 · see citing works
An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems
Muthubalaji, S., Muniyaraj, N. K., Rao, S. P. V. S., Thandapani, K., Mohan, P. R., Somasundaram, T., & Farhaoui, Y. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2023.9020022 · Cited by 71 · see citing works
DAMGAT-Based Interpretable Detection of False Data Injection Attacks in Smart Grids
Su, X., Deng, C., Yang, J., Li, F., Li, C., Fu, Y., & Dong, Z. Y. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3364665 · Cited by 69 · see citing works
A Subgrid-Oriented Privacy-Preserving Microservice Framework Based on Deep Neural Network for False Data Injection Attack Detection in Smart Grids
Yin, X., Zhu, Y., & Hu, J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3102332 · Cited by 68 · see citing works
Privacy-Preserved Cyberattack Detection in Industrial Edge of Things (IEoT): A Blockchain-Orchestrated Federated Learning Approach
Abdel‐Basset, M., Moustafa, N., & Hawash, H. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3167663 · Cited by 68 · see citing works
Detection and Prediction of FDI Attacks in IoT Systems via Hidden Markov Model
Moudoud, H., Mlika, Z., Khoukhi, L., & Cherkaoui, S. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3161479 · Cited by 67 · see citing works
Data Integrity Attack Detection Using Ensemble-Based Learning for Cyber–Physical Power Systems
Goyel, H., & Swarup, K. S. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3199305 · Cited by 66 · see citing works
A review of machine learning applications in IoT-integrated modern power systems
Farhoumandi, M., Zhou, Q., & Shahidehpour, M. (2020) · The Electricity Journal · doi:10.1016/j.tej.2020.106879 · Cited by 69 · see citing works
Cybersecurity Challenges in Smart Grid Systems: Current and Emerging Attacks, Opportunities, and Recommendations
Amanlou, S., Hasan, M. K., Mokhtar, U. A., Malik, K. M., Islam, S., Sheroz, K., Khan, M. A., & Khan, M. A. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3545153 · Cited by 70 · see citing works
Energy Theft Detection in Smart Grids: Taxonomy, Comparative Analysis, Challenges, and Future Research Directions
Ahmed, M., Khan, A., Ahmed, M., Tahir, M., Jeon, G., Fortino, G., & Piccialli, F. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105404 · Cited by 61 · see citing works
A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids
Haghshenas, S. H., Hasnat, M. A., & Naeini, M. (2023) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt51731.2023.10066446 · Cited by 60 · see citing works
An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids
Zideh, M. J., Khalghani, M. R., & Solanki, S. K. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.110407 · Cited by 60 · see citing works
False data injection threats in active distribution systems: A comprehensive survey
Husnoo, M. A., Anwar, A., Hosseinzadeh, N., Islam, S. N., Mahmood, A. N., & Doss, R. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.10.021 · Cited by 57 · see citing works
False Data-Injection Attack Detection in Cyber–Physical Systems With Unknown Parameters: A Deep Reinforcement Learning Approach
Liu, K., Zhang, H., Zhang, Y., & Sun, C. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3225236 · Cited by 57 · see citing works
Self-citation
A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept
Jafari, M., Kavousi‐Fard, A., Dabbaghjamanesh, M., & Karimi, M. (2022) · IEEE Access · doi:10.1109/access.2022.3195053 · Cited by 59 · see citing works
Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection
Pinto, S. J., Siano, P., & Parente, M. (2023) · Energies · doi:10.3390/en16041651 · Cited by 56 · see citing works
An integrated data-driven scheme for the defense of typical cyber–physical attacks
Wu, S., Jiang, Y., Luo, H., Zhang, J., Yin, S., & Kaynak, O. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108257 · Cited by 55 · see citing works
Data‐driven detection and identification of IoT‐enabled load‐altering attacks in power grids
Lakshminarayana, S., Sthapit, S., Jahangir, H., Maple, C. R., & Poor, H. V. (2022) · IET Smart Grid · doi:10.1049/stg2.12066 · Cited by 53 · see citing works
A Highly Discriminative Detector Against False Data Injection Attacks in AC State Estimation
Cheng, G., Lin, Y., Zhao, J., & Yan, J. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3141803 · Cited by 53 · see citing works
Detection of False Data Injection Attacks in Distribution Networks: A Vertical Federated Learning Approach
Kesici, M., Pal, B. C., & Yang, G. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3399396 · Cited by 58 · see citing works
Preventing False Data Injection Attacks in LFC System via the Attack-Detection Evolutionary Game Model and KF Algorithm
Zhang, Z., Hu, J., Lu, J., Cao, J., & Alsaadi, F. E. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3199881 · Cited by 50 · see citing works
Attack Power System State Estimation by Implicitly Learning the Underlying Models
Costilla-Enriquez, N., & Weng, Y. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3197770 · Cited by 49 · see citing works
Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids
Takiddin, A., Ismail, M., Atat, R., Davis, K., & Serpedin, E. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3286831 · Cited by 49 · see citing works
Machine learning for modern power distribution systems: Progress and perspectives
Marković, M., Bossart, M., & Hodge, B. S. (2023) · Journal of Renewable and Sustainable Energy · doi:10.1063/5.0147592 · Cited by 49 · see citing works
Experience-Driven Attack Design and Federated-Learning-Based Intrusion Detection in Industry 4.0
Tahir, B., Jolfaei, A., & Tariq, M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3133384 · Cited by 47 · see citing works
A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grids
Reda, H. T., Anwar, A., Mahmood, A. N., & Tari, Z. (2023) · ACM Computing Surveys · doi:10.1145/3592797 · Cited by 47 · see citing works
Trends in Smart Grid Cyber-Physical Security: Components, Threats, and Solutions
Manias, D. M., Saber, A. M., Radaideh, M. I., Gaber, A. T., Maniatakos, M., Zeineldin, H. H., Svetinović, D., & El-Saadany, E. F. (2024) · IEEE Access · doi:10.1109/access.2024.3477714 · Cited by 45 · see citing works
XTM: A Novel Transformer and LSTM-Based Model for Detection and Localization of Formally Verified FDI Attack in Smart Grid
Baul, A., Sarker, G. C., Sadhu, P. K., Yanambaka, V. P., & Abdelgawad, A. (2023) · Electronics · doi:10.3390/electronics12040797 · Cited by 44 · see citing works
Detection and reconstruction of measurements against false data injection and DoS attacks in distribution system state estimation: A deep learning approach
Raghuvamsi, Y., & Teeparthi, K. (2023) · Measurement · doi:10.1016/j.measurement.2023.112565 · Cited by 44 · see citing works
Big Data-Driven Detection of False Data Injection Attacks in Smart Meters
Unal, F. A., Almalaq, A., Ekici, S., & Glauner, P. (2021) · IEEE Access · doi:10.1109/access.2021.3122009 · Cited by 43 · see citing works
Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review
Zhang, X., Wu, Z. S., Sun, Q., Gu, W., Zheng, S., & Zhao, J. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114282 · Cited by 43 · see citing works
Machine Learning Based Multi-Agent System for Detecting and Neutralizing Unseen Cyber-Attacks in AGC and HVDC Systems
Roy, S. D., Debbarma, S., & Guerrero, J. M. (2022) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2022.3142055 · Cited by 43 · see citing works
Deep Latent Space Clustering for Detection of Stealthy False Data Injection Attacks Against AC State Estimation in Power Systems
Bhattacharjee, A., Mondal, A. K., Verma, A., Mishra, S., & Saha, T. K. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3216625 · Cited by 42 · see citing works
Detection of data-driven blind cyber-attacks on smart grid: A deep learning approach
Mukherjee, D. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104475 · Cited by 41 · see citing works
Edge Intelligence for Smart Grid: A Survey on Application Potentials
Gooi, H. B., Wang, T., & Tang, Y. (2023) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2022.02210 · Cited by 40 · see citing works
Spatio-temporal data-driven detection of false data injection attacks in power distribution systems
Musleh, A. S., Chen, G., Dong, Z. Y., Wang, C., & Chen, S. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108612 · Cited by 38 · see citing works
Locational Detection of False Data Injection Attacks in the Edge Space via Hodge Graph Neural Network for Smart Grids
Xia, W., Li, Y., Yu, L., & He, D. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3389948 · Cited by 38 · see citing works
Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach
Xu, W., Higgins, M., Wang, J., Jaimoukha, I. M., & Teng, F. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3231728 · Cited by 38 · see citing works
Self-citation
A Survey on the Applications of Semi-supervised Learning to Cyber-security
Mvula, P. K., Branco, P., Jourdan, G.-V., & Viktor, H. L. (2024) · ACM Computing Surveys · doi:10.1145/3657647 · Cited by 36 · see citing works
FDIA localization and classification detection in smart grids using multi-modal data and deep learning technique
Wang, J., Chen, H., Si, Y., Zhu, Y., Zhu, T., Yin, S., & Liu, B. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109572 · Cited by 36 · see citing works
Self-citation
Exploring the emerging role of large language models in smart grid cybersecurity: a survey of attacks, detection mechanisms, and mitigation strategies
Ibrahim, N., & Kashef, R. F. (2025) · Frontiers in Energy Research · doi:10.3389/fenrg.2025.1531655 · Cited by 44 · see citing works
Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
Yaacoub, J.-P. A., Noura, H., Salman, O., & Chahine, K. (2025) · Future Internet · doi:10.3390/fi17070318 · Cited by 45 · see citing works
A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders
Wang, Y., Zhou, Y., Ma, J., & Jin, Q. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111169 · Cited by 35 · see citing works
Privacy-preserving federated learning for detecting false data injection attacks on power system
Lin, W.-T., Chen, G., & Zhou, X. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.110150 · Cited by 35 · see citing works
Analysis of false data injection attacks in power systems: A dynamic Bayesian game-theoretic approach
Tian, M., Dong, Z., & Wang, X. (2021) · ISA Transactions · doi:10.1016/j.isatra.2021.01.011 · Cited by 35 · see citing works
Detection of false data injection attack in power information physical system based on SVM–GAB algorithm
Xiong, X., Hu, S., Sun, D., Hao, S., Li, H., & Lin, G. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.02.290 · Cited by 35 · see citing works
Impact Assessment and Defense for Smart Grids With FDIA Against AMI
Bi, J., Luo, F., Liang, G., Yang, X., He, S., & Dong, Z. Y. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3197682 · Cited by 34 · see citing works
Anomaly Detection in Power System State Estimation: Review and New Directions
Cooper, A., Bretas, A. S., & Meyn, S. (2023) · Energies · doi:10.3390/en16186678 · Cited by 34 · see citing works
Graph Autoencoder-Based Power Attacks Detection for Resilient Electrified Transportation Systems
Fahim, S. R., Atat, R., Keçeci, C., Takiddin, A., Ismail, M., Davis, K., & Serpedin, E. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3355094 · Cited by 34 · see citing works
A Review on Microgrid Protection Challenges and Approaches to Address Protection Issues
Baba, M., Nor, N. M., Shiekh, M. A., Alharthi, Y., Shutari, H., & Majeed, M. F. (2024) · IEEE Access · doi:10.1109/access.2024.3458047 · Cited by 34 · see citing works
An Optimization-Based Approach to Recover the Detected Attacked Grid Variables After False Data Injection Attack
Jorjani, M., Seifi, H., Varjani, A. Y., & Delkhosh, H. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3103556 · Cited by 34 · see citing works
Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs
Xie, J. H., Rahman, A., & Sun, W. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3337340 · Cited by 33 · see citing works
Deep generative models in energy system applications: Review, challenges, and future directions
Zhang, X., Glaws, A., Cortiella, A., Emami, P., & King, R. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.125059 · Cited by 33 · see citing works
A Semisupervised Soft-Sensor of Just-in-Time Learning With Structure Entropy Clustering and Applications for Industrial Processes Monitoring
Li, D., Liu, Y., Huang, D., & Xu, C. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3217028 · Cited by 33 · see citing works
False Data Injection Attacks on LFC Systems: An AI-Based Detection and Countermeasure Strategy
Zhang, Z., Hu, J., Lu, J., Cao, J., & Yu, J. (2023) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2023.3307821 · Cited by 31 · see citing works
On the Use of Reinforcement Learning for Attacking and Defending Load Frequency Control
Mohamed, A. S., & Kundur, D. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3343100 · Cited by 31 · see citing works
A Review on Cyber Security and Anomaly Detection Perspectives of Smart Grid
Ravinder, M., & Kulkarni, V. (2023) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit55814.2023.10060871 · Cited by 31 · see citing works
Dual-hybrid intrusion detection system to detect False Data Injection in smart grids
Mohammed, S. H., Singh, M. S. J., Al-Jumaily, A. H. J., Islam, M. T., Islam, M. S., Alenezi, A. M., & Soliman, M. S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0316536 · Cited by 30 · see citing works
Attack Detection and Localization in Smart Grid with Image-based Deep Learning
Mohammadpourfard, M., Genc, I. V. M., Lakshminarayana, S., & Konstantinou, C. (2021) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm51999.2021.9631994 · Cited by 30 · see citing works
Locational False Data Injection Attack Detection in Smart Grid Using Recursive Variational Graph Autoencoder
Wang, Y., Lu, Z., Ma, J., & Jin, Q. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3526672 · Cited by 29 · see citing works
False Data Injection Attack Detection and Localization Framework in Power Distribution Systems Using a Novel Ensemble of CNNs and Explainable Artificial Intelligence
Dehbozorgi, M. R., Rastegar, M., & Arani, M. F. M. (2025) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2025.3532917 · Cited by 28 · see citing works
Data/Model Jointly Driven High-Quality Case Generation for Power System Dynamic Stability Assessment
Zhu, L., & Hill, D. J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3123823 · Cited by 28 · see citing works
A False Data Injection Attack Approach Without Knowledge of System Parameters Considering Measurement Noise
Yang, H., & Wang, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288983 · Cited by 28 · see citing works
An Enhanced Energy Management System Including a Real-Time Load-Redistribution Threat Analysis Tool and Cyber-Physical SCED
Kaviani, R., & Hedman, K. W. (2021) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3135357 · Cited by 27 · see citing works
Anomaly Detection in Critical-Infrastructures using Autoencoders: A Survey
Mavikumbure, H. S., Wickramasinghe, C. S., Marino, D., Cobilean, V., & Manic, M. (2022) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon49645.2022.9968505 · Cited by 27 · see citing works
Locational detection of the false data injection attacks via semi-supervised multi-label adversarial network
Feng, H., Han, Y., Li, K., Si, F., & Zhao, Q. (2023) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.109682 · Cited by 26 · see citing works
A review on distribution system state estimation uncertainty issues using deep learning approaches
Raghuvamsi, Y., & Teeparthi, K. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113752 · Cited by 25 · see citing works
Cybersecurity challenges in power networks with distributed energy resources: A comprehensive survey
Ruiz, G. E. M., Marasini, G., Zhihua, Q., Kundu, S., & Pushpak, S. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116100 · Cited by 25 · see citing works
Detection of false data injection attacks leading to line congestions using Neural networks
He, Z., Khazaei, J., Moazeni, F. K., & Freihaut, J. D. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.103861 · Cited by 25 · see citing works
Detection and Prevention of False Data Injection Attacks in the Measurement Infrastructure of Smart Grids
Shahid, M. A., Ahmad, F., Albogamy, F. R., Hafeez, G., & Ullah, Z. (2022) · Sustainability · doi:10.3390/su14116407 · Cited by 25 · see citing works
A Novel Dynamic Watermarking-Based EKF Detection Method for FDIAs in Smart Grid
Li, X., Wang, Z., Zhang, C., Du, D., & Fei, M. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105704 · Cited by 25 · see citing works
Advanced Machine Learning in Smart Grids: An overview
Noura, H., Yaacoub, J.-P. A., Salman, O., & Chehab, A. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2025.05.002 · Cited by 24 · see citing works
Cybersecurity in Power Electronics Using Minimal Data – A Physics-Informed Spline Learning Approach
Kurukuru, V. S. B., Khan, M. A., & Sahoo, S. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3180943 · Cited by 24 · see citing works

False Data Injection Attack Detection in Smart Grid Based on Learnable Unified Neighborhood-Based Anomaly Ranking
Luo, J., Guo, H., Kong, H., Hu, X., Li, S., Zuo, D., Li, G., Ren, Z., Li, Y., Zhang, W., et al. (2025) · Electronics · doi:10.3390/electronics14173396 · Cited by 2 · see citing works
Detecting False Data Injection Attacks in Smart Grids: A Transformer-Based Self-Supervised Learning Approach
Liu, Y. H., Pradeep, A., Stucky, T., Wu, H., Wiegers, M., & Liu, B. (2025) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm52009.2025.11225553
Data-Driven Detection of False Data Injection Attacks in Smart Grids using Self-Attention Enabled Conditional GAN with Gradient Penalty
Murugesan, S., Jethmalani, C. H. R., Sam, K. N., Venkadesan, A., & J, S. K. S. (2026) · IEEE Latin America Transactions · doi:10.1109/tla.2026.11435592 · Cited by 2 · see citing works
Identification scheme of false data injection attack based on deep learning algorithms for smart grids
Merza, M. E., Hussein, S. H., & Ali, Q. I. (2023) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v30.i1.pp219-228 · Cited by 6 · see citing works
Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning
Niu, X., Li, J., Sun, J., & Tomsovic, K. L. (2019) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt.2019.8791598 · Cited by 144 · see citing works

Authors: Zhang, Y., Wang, J., & Chen, B.
Affiliations: Southern Methodist University (US)
Argonne National Laboratory (US)
ORCID: Zhang, Ying
Wang, Jianhui
Chen, Bo
Venue: IEEE Transactions on Smart Grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-01
Volume: 12
Issue: 1
Pages: 623-634
ISSN: 1949-3053 (ISSN-L)
1949-3061
DOI: 10.1109/tsg.2020.3010510
OpenAlex: W3044239919
Cited by: 332
References: 35
Citations by year: 2026: 40 · 2025: 83 · 2024: 66 · 2023: 61 · 2022: 58 · 2021: 20 · 2020: 4
FWCI: 17.68
Type: journal article
Language: en
Open access: green
www.osti.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Smart Grid (publishedVersion)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Topics: Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
Physical Sciences › Engineering › Electrical and Electronic Engineering › Electricity Theft Detection Techniques
Keywords: false data injection attacks, smart grid security, bad data detection, distribution systems, autoencoder, generative adversarial networks, anomaly detection, semi-supervised learning, measurement data, cyberattacks

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks