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Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach
Open accessAbstract
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.
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“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]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
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 20,507
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
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015) · arXiv · doi:10.48550/arxiv.1511.05644 · Cited by 1,579
Distribution System Modeling and Analysis
Kersting, W. (2012) · Electric Power Engineering Series · doi:10.1201/b12056-31 · Cited by 1,241
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
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
He, Y., Mendis, G. J., & Wei, J. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2703842 · Cited by 858
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
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
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
Self-citationOn 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
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
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
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 386
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
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
Ren, C., & Xu, Y. H. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2922671 · Cited by 248
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
Distribution System Modeling and Analysis
Kersting, W. H. (2006) · CRC Press eBooks · doi:10.1201/9781420006162 · Cited by 232
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
Self-citationSemisupervised Autoencoders for Speech Emotion Recognition
Deng, J., Xu, X., Zhang, Z., Frühholz, S., & Schuller, B. W. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2759338 · Cited by 152
Foroutan, S. A., & Salmasi, F. R. (2017) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/iet-cps.2017.0013 · Cited by 151
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
Self-citationZhuang, P., Deng, R., & Liang, H. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2895306 · Cited by 108
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
Zhang, Y., Wang, J.-H., & Li, Z.-S. (2020) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2926445 · Cited by 82
Self-citationUncertainty 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
Self-citationCai, L., Thornhill, N. F., Kuenzel, S., & Pal, B. C. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2783242 · Cited by 67
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
Self-citationProbabilistic 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
Self-citationDai, Q., Shi, L., & Ni, Y. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2899983 · Cited by 55
Memorization in Overparameterized AutoencodersPreprint
Radhakrishnan, A., Yang, K., Belkin, M., & Uhler, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10333 · Cited by 50
Research works printed but not linked: 3
- 2011, Smart Meters and Smart Meter Systems A Metering Industry Perspective
- 2017, IEEE Test Feeder Specifications
- 2016, A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part II)
Cited by
Sorted by citations
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
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
Jafari, M., Kavousi‐Fard, A., Chen, T., & Karimi, M. (2023) · IEEE Access · doi:10.1109/access.2023.3241588 · Cited by 428
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
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
Metadata
- Authors
- Zhang, Y., Wang, J., & Chen, B.
- Affiliations
- Southern Methodist University (US)
- Argonne National Laboratory (US)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2021-01
- Volume
- 12
- Issue
- 1
- Pages
- 623-634
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W3044239919
- Cited by
- 332
- References
- 35
- Citations by year
- 2026: 40 · 2025: 83 · 2024: 66 · 2023: 61 · 2022: 58 · 2021: 20
- FWCI
- 17.68
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- 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
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