A survey on detecting false data injection in power systems with auto-encoder based deep learning methods

Document Type : Original Article

Authors

1 PhD student, Computer Education Department, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran

2 Associate Professor, Electrical Department, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran

3 Assistant Professor, Department of Computer Education, Technical and Engineering Faculty, Lorestan University, Khorram Abad, Lorestan, Iran

Abstract

The number of cyber-attacks affecting power systems and leading to physical and economic damages has grown rapidly over the last decade. Among the most significant types of cyber-attacks, are the class of false data injection attacks (FDIAs) which affect the power network monitoring systems. FDIAs endanger the power grid with manipulating the power system state estimation (SE). Also, the electricity theft has recently become another purpose of the FDAIs. Machine learning based methods are known as one of the FDIAs detection approaches. In this paper, first, using the deep auto-encoder method, the dimensions of the problem and the number of data entry for problem classification and detection are reduced. Then, by employing the support vector machine (SVM) approach and the data learning method, the procedure of cyber-attack detection is formed. Also, the precision of the proposed approach is improved by changing the
number of data being trained. The presented method is evaluated on the IEEE 14 and 118 bus systems. The obtained simulation results demonstrate that the new method can successfully be applied for an accurate and effective detection of FDIAs.

Keywords

Smiley face

References

[1]    S. Jacob, H. Karimipour, and A. Dehghantanha, "Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection," In 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE), 2019.
[2]    M. Rahimi, F. Faghihi, B. Mozafari, "Control Strategy to Maintain Stability of Micro-grids, During Occurring Cyber Attacks on the Power Grid," Scientific Journal of Electronic and Cyber Defense, vol. 5, no. 2, pp. 47-58, 2017.
[3]    A. Ashok, M. Govindarasu, and J. Wang, "Cyber-physical Attack-resilient Wide-area Monitoring, Protection, and Control for the Power Grid," Proceedings of the IEEE, vol. 105, no. 7. pp. 1389-1407, 2017.
[4]    A. Qiu, Zh. Ding, and Sh. Wang, "A Descriptor System Design Framework for False Data Injection Attack Toward Power Systems," Electric Power Systems Research, vol. 192, pp. 106932, 2020.
[5]    Y. Liu, P. Ning, and M. K. Reiter, "False Data Injection Attacks against State Estimation in Electric Power Grids," ACM Transactions on Information and System Security (TISSEC), vol. 14, no. 1, pp. 1-33, 2011.
[6]    Y. Zhang, J. Wang, and B. Chen, "Detecting False Data Injection Attacks in Smart Grids: A Semi-supervised Deep Learning Approach," IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 623-634, 2020.
[7]    D. B. Rawat and Ch. Bajracharya, "Detection of False Data Injection Attacks in Smart Grid Communication Systems," IEEE Signal Processing Letters, vol. 22, no. 10, pp. 1652-1656, 2015.
[8]    A.-Y. Lu and G.-H. Yang, "False Data Injection Attacks against State Estimation in the Presence of Sensor Failures," Information Sciences, vol. 508, pp. 92-104, 2020.
[9]    B. Tang, J. Yan, S. Kay, and H. He, "Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise," In 2016 IEEE Conference on Communications and Network Security (CNS), 2016.
[10]  Y. Li and Y. Wang, "Developing Graphical Detection Techniques for Maintaining State Estimation Integrity against False Data Injection Attack in Integrated Electric Cyber-physical System," Journal of Systems Architecture, vol. 105, pp. 101705, 2020.
[11]  Y. Chakhchoukh, S. Liu, M. Sugiyama, and H. Ishii, "Statistical Outlier Detection for Diagnosis of Cyber Attacks in Power State Estimation," In 2016 IEEE Power and Energy Society General Meeting (PESGM), 2016.
[12]  S. Mohammadi, V. Desai, and H. Karimipour, "Multivariate Mutual Information-based Feature Selection for Cyber Intrusion Detection," In 2018 IEEE Electrical Power and Energy Conference (EPEC), 2018.
[13]  M. Ozay, I. Esnaola, F. Tunay Yarman Vural, S. R. Kulkarni, and H. Vincent Poor, "Machine Learning Methods for Attack Detection in the Smart Grid," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1773-1786, 2015.
[14]  M. Esmalifalak, L. Liu, N. Nguyen, R. Zheng, and Zh. Han, "Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid," IEEE Systems Journal, vol. 11, no. 3, pp. 1644-1652, 2014.
[15]  M. Ahmed and A.-S. Kh. Pathan, "False Data Injection Attack (FDIA): An Overview and New Metrics for Fair Evaluation of Its Countermeasure," Complex Adaptive Systems Modeling, vol. 8, pp. 1-14, 2020.
[16]  S. Waghmare, F. Kazi, and N. Singh, "Data Driven Approach to Attack Detection in a Ccyber-physical Smart Grid System," In 2017 Indian Control Conference (ICC), 2017.
[17]  H. Karimipour and V. Dinavahi, "On False Data Injection Attack against Dynamic State Estimation on Smart Power Grids," In 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE), 2017.
[18]  D. B. Rawat, and Ch. Bajracharya, "Detection of False Data Injection Attacks in Smart Grid Communication Systems," IEEE Signal Processing Letters, vol. 22, no. 10, pp. 1652-1656, 2015.
[19]  J. Yan, B. Tang, and H. He, "Detection of False Data Attacks in Smart Grid with Supervised Learning," In 2016 International Joint Conference on Neural Networks (IJCNN), 2016.
[20]  L. Mili, M. G. Cheniae, N. S. Vichare, and P. J. Rousseeuw, "Robust State Estimation Based on Projection Statistics [of Power Systems]." IEEE Transactions on Power Systems, vol. 11, no. 2, pp. 1118-1127, 1996.
[21]  G. Liang, J. Zhao, F. Luo, S. R. Weller, and Zh. Yang Dong, "AR of False Data Injection Attacks against Modern Power Systems," IEEE Transactions on Smart Grid, vol. 8, no. 4, pp.1630-1638, 2016.
[22]  G. Liang, S. R. Weller, J. Zhao, F.Luo, and Zh. Yang Dong, "The 2015 Ukraine Blackout: Implications for False Data Injection Attacks," IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 3317-3318, 2016.
[23]  Y. Wang, H. Yao, and S. Zhao, "Auto-encoder Based Dimensionality Reduction," Neurocomputing, vol. 184, pp. 232-242, 2016.
[24]  S. M. Erfani, S. Rajasegarar, Sh. Karunasekera, and Ch. Leckie, "High-dimensional and Large-scale Anomaly Detection Using a Linear One-class SVM with Deep Learning," Pattern Recognition, vol. 58, pp. 121-134, 2016.
[25]  A. Sayghe, Y. Hu, I. Zografopoulos, X. R.  Liu, R. G. Dutta, Y. Jin, and Ch. Konstantinou, "Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems," IET Smart Grid, 2020.
[26]  A. Kajal, and S. K. Nandal,  “A Hybrid Approach for Cyber Security: Improved Intrusion Detection System Ising Ann-svm,” Indian Journal of Computer Science and Engineering, vol. 11, no. 4,pp. 325-412, [M1] 2020.

Files

History

  • Receive Date: 28 January 2021
  • Revise Date: 05 April 2022
  • Accept Date: 11 December 2021
  • Publish Date: 23 September 2022

Share

How to cite

Statistics