Defense strategy selection based on incomplete information game for the false data injection attack

References

• Ahmadi, A., Nabipour, M., Taheri, S., Mohammadi-Ivatloo, B., & Vahidinasab, V. (2023). A new false data injection attack detection model for cyberattack resilient energy forecasting. IEEE Transactions on Industrial Informatics, 19(1), 371–381. https://doi.org/10.1109/TII.2022.3151748
   Google Scholar
• Chaojun, G., Jirutitijaroen, P., & Motani, M. (2015). Detecting false data injection attacks in AC state estimation. IEEE Transactions on Smart Grid, 6(5), 2476–2483. https://doi.org/10.1109/TSG.2015.2388545
   Web of Science ®Google Scholar
• Choraria, M., Chattopadhyay, A., Mitra, U., & Strom, E. G. (2022). Design of false data injection attack on distributed process estimation. IEEE Transactions on Information Forensics and Security, 17, 670–683. https://doi.org/10.1109/TIFS.2022.3146078
   Google Scholar
• Dehghani, M., Ghiasi, M., Niknam, T., Kavousi-Fard, A., Tajik, E., Padmanaban, S., & Aliev, H. (2021). Cyber attack detection based on wavelet singular entropy in AC smart islands: False data injection attack. IEEE Access, 9, 16488–16507. https://doi.org/10.1109/ACCESS.2021.3051300
   Google Scholar
• Fu, X., Chen, G., & Yang, D. (2020). Local false data injection attack theory considering isolation physical-protection in power systems. IEEE Access, 8, 103285–103290. https://doi.org/10.1109/ACCESS.2020.2999585
   Google Scholar
• Griffioen, P., Weerakkody, S., & Sinopoli, B. (2021). A moving target defense for securing cyber-physical systems. IEEE Transactions on Automatic Control, 66(5), 2016–2031. https://doi.org/10.1109/TAC.2020.3005686
   Google Scholar
• Haimes, Y. Y., & Chittester, C. G. (2005). A roadmap for quantifying the efficacy of risk management of information security and interdependent SCADA systems. Journal of Homeland Security and Emergency Management, 2(2), https://doi.org/10.2202/1547-7355.1117
   Google Scholar
• He, Y., Mendis, G. J., & Wei, J. (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), 2505–2516. https://doi.org/10.1109/TSG.2017.2703842
   Web of Science ®Google Scholar
• Li, Y., Huang, R., & Ma, L. (2021). False data injection attack and defense method on load frequency control. IEEE Internet of Things Journal, 8(4), 2910–2919. https://doi.org/10.1109/JIOT.2020.3021429
   Google Scholar
• Li, T., Wang, J.-D., Chen, Y., & Wang, N. (2017, March 25–26). A multi-stage game approach applied to network security risk controlling. 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, 2518–2522. https://doi.org/10.1109/IAEAC.2017.8054477
   Google Scholar
• Li, J. Y., Wu, Y. D., Li, Y., Zhang, Z. W., Fouad, H., & Altameem, T. (2023). A network security prediction method based on attack defense tree. Journal of Nanoelectronics and Optoelectronics, 18(3), 357–366. https://doi.org/10.1166/jno.2023.3398
   Google Scholar
• Liang, G. Q., Weller, S. R., Zhao, J. H., Luo, F. J., & Dong, Z. Y. (2017). The 2015 Ukraine blackout: Implications for false data injection attacks. IEEE Transactions on Power Systems, 32(4), 3317–3318. https://doi.org/10.1109/TPWRS.2016.2631891
   Web of Science ®Google Scholar
• Liu, X., Li, Z., & Li, Z. (2017). Optimal protection strategy against false data injection attacks in power systems. IEEE Transactions on Smart Grid, 8(4), 1802–1810. https://doi.org/10.1109/TSG.2015.2508449
   Google Scholar
• Liu, S. Z., Shao, C. W., Li, Y. F., & Yang, Z. (2022). Game attack-defense graph approach for modeling and analysis of cyberattacks and defenses in local metering system. IEEE Transactions on Automation Science and Engineering, 19(3), 2607–2619. https://doi.org/10.1109/TASE.2021.3093082
   Google Scholar
• Liu, X., Tang, D., & Dai, Z. (2022). A Bayesian game approach for demand response management considering incomplete information. Journal of Modern Power Systems and Clean Energy, 10(2), 492–501. https://doi.org/10.35833/MPCE.2020.000288
   Google Scholar
• Liu, X. K., Wang, S. Q., Chi, M., Liu, Z. W., & Wang, Y. W. (2024). Resilient secondary control and stability analysis for DC microgrids under mixed cyber attacks. IEEE Transactions on Industrial Electronics, 71(2), 1938–1947. https://doi.org/10.1109/TIE.2023.3262893
   Web of Science ®Google Scholar
• Luo, X., Li, Y., Wang, X., & Guan, X. (2021). Interval observer-based detection and localization against false data injection attack in smart grids. IEEE Internet of Things Journal, 8(2), 657–671. https://doi.org/10.1109/JIOT.2020.3005926
   Google Scholar
• Qu, Z., Dong, Y., Qu, N., Li, H., Cui, M., Bo, X., Wu, Y., & Mugemanyi, S. (2021). False data injection attack detection in power systems based on cyber-physical attack genes. Frontiers in Energy Research, 9, https://doi.org/10.3389/fenrg.2021.644489
   Google Scholar
• Qu, Z., Zhang, J., Wang, Y., Georgievitch, P. M., & Guo, K. (2022). False data injection attack detection and improved WLS power system state estimation based on node trust. Journal of Electrical Engineering and Technology, 17(2), 803–817. https://doi.org/10.1007/s42835-021-00923-1
   Google Scholar
• Shao, C. W., & Li, Y. F. (2021). Optimal defense resources allocation for power system based on bounded rationality game theory analysis. IEEE Transactions on Power Systems, 36(5), 4223–4234. https://doi.org/10.1109/TPWRS.2021.3060009
   Google Scholar
• Tran, H.-Y., Hu, J., Yin, X., & Pota, H. R. (2023). An efficient privacy-enhancing cross-silo federated learning and applications for false data injection attack detection in smart grids. IEEE Transactions on Information Forensics and Security, 18, 2538–2552. https://doi.org/10.1109/TIFS.2023.3267892
   Google Scholar
• Wu, H. C., Gao, Q. Y., Tao, X. F., Zhang, N., Chen, D. J., & Han, Z. (2022). Differential game approach for attack-defense strategy analysis in internet of things networks. IEEE Internet of Things Journal, 9(12), 10340–10353. https://doi.org/10.1109/JIOT.2021.3122115
   Google Scholar
• Ye, D., & Zhang, T. Y. (2020). Summation detector for false data-injection attack in cyber-physical systems. IEEE Transactions on Cybernetics, 50(6), 2338–2345. https://doi.org/10.1109/TCYB.2019.2915124
   PubMedGoogle Scholar
• Yi, N., Wang, Q., Yan, L., Tang, Y., & Xu, J. (2021). A multi-stage game model for the false data injection attack from attacker's perspective. Sustainable Energy, Grids and Networks, 28, 100541. https://doi.org/10.1016/j.segan.2021.100541
   Google Scholar
• Yi, N., Xu, J. J., Yan, L. M., & Huang, L. (2020). Task optimization and scheduling of distributed cyber-physical system based on improved ant colony algorithm. Future Generation Computer Systems-the International Journal of Escience, 109, 134–148. https://doi.org/10.1016/j.future.2020.03.051
   Google Scholar
• Yin, X., Zhu, Y., & Hu, J. (2022). A subgrid-oriented privacy-preserving microservice framework based on deep neural network for false data injection attack detection in smart grids. IEEE Transactions on Industrial Informatics, 18(3), 1957–1967. https://doi.org/10.1109/TII.2021.3102332
   Google Scholar
• Zhao, Z., Huang, Y., Zhen, Z., & Li, Y. (2021). Data-driven false data-injection attack design and detection in cyber-physical systems. IEEE Transactions on Cybernetics, 51(12), 6179–6187. https://doi.org/10.1109/TCYB.2020.2969320
   PubMedGoogle Scholar