Abstract
Power grid cyber-physical systems face a variety of cyber-attacks. Machine learning based stealthy false data injection attacks are explored in this paper. To avoid the detection of the Bad Data Detector, two issues are considered in the machine learning based attack strategy. One is how to use machine learning to generate suitable attack vectors based on eavesdropping measurements. The other is how to improve the robustness of the attack strategy if there are corrupted measurements or outliers. Considering these two problems, a robust linear regression based false data injection attack strategy is proposed. Moreover, a more sparse attack strategy is also explored to further reduce the cost of attackers. Simulations conducted on the IEEE 14-bus system verify the effectiveness of the attack strategies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mori, K.: Autonomous decentralized systems: concept, data field architecture and future trends. In: Proceedings ISAD 93: International Symposium on Autonomous Decentralized Systems, pp. 28–34. IEEE (1993)
Vallee, M., Merdan, M., Lepuschitz, W., Koppensteiner, G.: Decentralized reconfiguration of a flexible transportation system. IEEE Trans. Industr. Inf. 7(3), 505–516 (2011)
Xu, Q., et al.: A decentralized dynamic power sharing strategy for hybrid energy storage system in autonomous DC microgrid. IEEE Trans. Industr. Electron. 64(7), 5930–5941 (2017)
Liu, Y., Ning, P., Reiter, M.K.: False data injection attacks against state estimation in electric power grids. ACM Trans. Inf. Syst. Secur. (TISSEC) 14(1), 13 (2011)
Tian, J., Wang, B., Li, X.: Data-driven and low-sparsity false data injection attacks in smart grid. Secur. Commun. Netw. 2018, 11 (2018). Art. no. 8045909
JiWei, T., BuHong, W., FuTe, S., Shuaiqi, L.: Stealthy false data injection attacks using matrix recovery and independent component analysis in smart grid. In: IOP Conference Series: Materials Science and Engineering, vol. 199, no. 1, p. 012034. IOP Publishing (2017)
Xie, L., Mo, Y., Sinopoli, B.: False data injection attacks in electricity markets. In: First IEEE International Conference on Smart Grid Communications, pp. 226–231 (2010)
Tajer, A.: False data injection attacks in electricity markets by limited adversaries: stochastic robustness. IEEE Trans. Smart Grid 10, 128–138 (2017)
Che, L., Liu, X., Ding, T., Li, Z.: Revealing impacts of cyber attacks on power grids vulnerability to cascading failures. IEEE Trans. Circuits Syst. II Express Briefs 66, 1058–1062 (2018)
Esmalifalak, M., Liu, L., Nguyen, N., Zheng, R., Han, Z.: Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst. J. 11(3), 1644–1652 (2017)
Ozay, M., Esnaola, I., Vural, F.T.Y., Kulkarni, S.R., Poor, H.V.: Machine learning methods for attack detection in the smart grid. IEEE Trans. Neural Netw. Learn. Syst. 27(8), 1773–1786 (2016)
Kurt, M.N., Ogundijo, O., Li, C., Wang, X.: Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach, arXiv preprint arXiv:1809.05258 (2018)
Niu, X., Sun, J.: Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning, arXiv preprint arXiv:1808.01094 (2018)
Ayad, A., Farag, H.E.Z., Youssef, A., El-Saadany, E.F.: Detection of false data injection attacks in smart grids using recurrent neural networks. In: IEEE Power & Energy Society Innovative Smart Grid Technologies Conference, pp. 1–5 (2018)
Ashrafuzzaman, M., et al.: Detecting stealthy false data injection attacks in power grids using deep learning. In: 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 219–225. IEEE (2018)
Zhou, L., Ouyang, X., Ying, H., Han, L., Cheng, Y., Zhang, T.: Cyber-attack classification in smart grid via deep neural network. In: Proceedings of the 2nd International Conference on Computer Science and Application Engineering, p. 90. ACM (2018)
Nawaz, R., Shahid, M.A., Qureshi, I.M., Mehmood, M.H.: Machine learning based false data injection in smart grid. In: 2018 1st International Conference on Power, Energy and Smart Grid (ICPESG), pp. 1–6. IEEE (2018)
Li, H., Lai, L., Zhang, W.: Communication requirement for reliable and secure state estimation and control in smart grid. IEEE Trans. Smart Grid 2(3), 476–486 (2011)
Anwar, A., Mahmood, A.N., Pickering, M.: Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements. J. Comput. Syst. Sci. 83(1), 58–72 (2017)
Yu, C., Yao, W.: Robust linear regression: a review and comparison. Commun. Stat. Simul. Comput. 46(8), 6261–6282 (2017)
Load Data: Market and Operational Data (NYISO). http://www.nyiso.com/public/marketsoperations/index.jsp
Chaojun, G., Jirutitijaroen, P., Motani, M.: Detecting false data injection attacks in ac state estimation. IEEE Trans. Smart Grid 6(5), 2476–2483 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tian, J., Wang, B., Li, T., Shang, F., Cao, K., Li, J. (2019). Stealthy and Sparse False Data Injection Attacks Based on Machine Learning. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11982. Springer, Cham. https://doi.org/10.1007/978-3-030-37337-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-37337-5_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37336-8
Online ISBN: 978-3-030-37337-5
eBook Packages: Computer ScienceComputer Science (R0)