References
- Abdullah, A. (2017). Ultrafast transmission line fault detection using a dwt-based ann. IEEE Transactions on Industry Applications, 54(2), 1182–1193. https://doi.org/10.1109/TIA.2017.2774202
- Ai, X., Chen, Y.-Y., & Zhang, Y. (2020). Spherical formation tracking control of non-holonomic aircraft-like vehicles in a spatiotemporal flowfield. Journal of the Franklin Institute, 357(7), 3924–3952. https://doi.org/10.1016/j.jfranklin.2020.01.002
- Ao, W., Song, Y., & Wen, C. (2016). Adaptive cyber-physical system attack detection and reconstruction with application to power systems. IET Control Theory & Applications, 10(12), 1458–1468. https://doi.org/10.1049/cth2.v10.12
- Boyaci, O., Umunnakwe, A., Sahu, A., Narimani, M. R., Ismail, M., Davis, K. R., & Serpedin, E. (2021). Graph neural networks based detection of stealth false data injection attacks in smart grids. IEEE Systems Journal, 16(2), 2946–2957. https://doi.org/10.1109/JSYST.2021.3109082
- Cao, J., Wang, D., Qu, Z., Cui, M., Xu, P., Xue, K., & Hu, K. (2020). A novel false data injection attack detection model of the cyber-physical power system. IEEE Access, 8, 95109–95125. https://doi.org/10.1109/Access.6287639
- Cao, Z., Niu, Y., & Zou, Y. (2019). Adaptive neural sliding mode control for singular semi-markovian jump systems against actuator attacks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(3), 1523–1533.
- Deng, R., Xiao, G., & Lu, R. (2017). Defending against false data injection attacks on power system state estimation. IEEE Transactions on Industrial Informatics, 13(1), 198–207. https://doi.org/10.1109/TII.2015.2470218
- Deng, R., Xiao, G., Lu, R., Liang, H., & Vasilakos, A. V. (2016). False data injection on state estimation in power systems attacks, impacts, and defense: A survey. IEEE Transactions on Industrial Informatics, 13(2), 411–423. https://doi.org/10.1109/TII.2016.2614396
- Dibaji, S. M., Pirani, M., Flamholz, D. B., Annaswamy, A. M., K. H. Johansson, & Chakrabortty, A. (2019). A systems and control perspective of cps security. Annual Reviews in Control, 47, 394–411. https://doi.org/10.1016/j.arcontrol.2019.04.011
- Foroutan, S. A., & Salmasi, F. R. (2017). Detection of false data injection attacks against state estimation in smart grids based on a mixture gaussian distribution learning method. IET Cyber-Physical Systems: Theory & Applications, 2(4), 161–171. https://doi.org/10.1049/cps2.v2.4
- Guo, Z., Shi, D., Johansson, K. H., & Shi, L. (2016). Optimal linear cyber-attack on remote state estimation. IEEE Transactions on Control of Network Systems, 4(1), 4–13. https://doi.org/10.1109/TCNS.2016.2570003
- 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
- Hu, L., Wang, Z., Han, Q.-L., & Liu, X. (2018). State estimation under false data injection attacks: Security analysis and system protection. Automatica, 87, 176–183. https://doi.org/10.1016/j.automatica.2017.09.028
- Hua, Y., Chen, F., Deng, S., Duan, S., & Wang, L. (2020). Secure distributed estimation against false data injection attack. Information Sciences, 515, 248–262. https://doi.org/10.1016/j.ins.2019.12.016
- Huang, L., Zhou, M., Hao, K., & Hou, E. (2019). A survey of multi-robot regular and adversarial patrolling. IEEE/CAA Journal of Automatica Sinica, 6(4), 894–903. https://doi.org/10.1109/JAS.6570654
- James, J., Hou, Y., & Li, V. O. (2018). Online false data injection attack detection with wavelet transform and deep neural networks. IEEE Transactions on Industrial Informatics, 14(7), 3271–3280. https://doi.org/10.1109/TII.2018.2825243
- Jana, S., & De, A. (2017). A novel zone division approach for power system fault detection using ann-based pattern recognition technique. Canadian Journal of Electrical and Computer Engineering, 40(4), 275–283. https://doi.org/10.1109/CJECE.2017.2751661
- Jiang, Y., Niu, B., Wang, X., Zhao, X., Wang, H., & Yan, B. (2023). Distributed finite-time consensus tracking control for nonlinear multi-agent systems with fdi attacks and application to single-link robots. IEEE Transactions on Circuits and Systems II: Express Briefs, 70(4), 1505–1509.
- Kim, H., & Kim, Y. (2014). Trajectory optimization for unmanned aerial vehicle formation reconfiguration. Engineering Optimization, 46(1), 84–106. https://doi.org/10.1080/0305215X.2012.748048
- Lee, U., Magistretti, E., Gerla, M., Bellavista, P., Lió, P., & Lee, K.-W. (2009). Bio-inspired multi-agent data harvesting in a proactive urban monitoring environment. Ad Hoc Networks, 7(4), 725–741. https://doi.org/10.1016/j.adhoc.2008.03.009
- Lewis, F. L., Hengster-Movric, K., Zhang, H., & Das, A. (2014). Cooperative control of multi-agent systems: Optimal and adaptive design approaches. London, U.K.: Springer.
- Li, S. E., Zheng, Y., Li, K., Wu, Y., Hedrick, J. K., Gao, F., & Zhang, H. (2017). Dynamical modeling and distributed control of connected and automated vehicles: Challenges and opportunities. IEEE Intelligent Transportation Systems Magazine, 9(3), 46–58. https://doi.org/10.1109/MITS.2017.2709781
- Li, Y., Wei, X., Li, Y., Dong, Z., & Shahidehpour, M. (2022). Detection of false data injection attacks in smart grid: A secure federated deep learning approach. IEEE Transactions on Smart Grid, 13(6), 4862–4872. https://doi.org/10.1109/TSG.2022.3204796
- Li, Y.-G., & Yang, G.-H. (2019). Optimal stealthy false data injection attacks in cyber-physical systems. Information Sciences, 481, 474–490. https://doi.org/10.1016/j.ins.2019.01.001
- Liang, G., Weller, S. R., Zhao, J., Luo, F., & Dong, Z. Y. (2016). 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
- Mao, S., Dong, Z., Schultz, P., Tang, Y., Meng, K., Dong, Z. Y., & Qian, F. (2019). A finite-time distributed optimization algorithm for economic dispatch in smart grids. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4), 2068–2079. https://doi.org/10.1109/TSMC.6221021
- Meng, M., Xiao, G., & Li, B. (2020). Adaptive consensus for heterogeneous multi-agent systems under sensor and actuator attacks. Automatica, 122, Article 109242. https://doi.org/10.1016/j.automatica.2020.109242
- Milošević, J., Sandberg, H., & Johansson, K. H. (2019). Estimating the impact of cyber-attack strategies for stochastic networked control systems. IEEE Transactions on Control of Network Systems, 7(2), 747–757.
- Niu, X., Li, J., Sun, J., & Tomsovic, K. (2019). Dynamic detection of false data injection attack in smart grid using deep learning. In 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1–6). IEEE.
- Pang, Z.-H., Fan, L.-Z., Dong, Z., Han, Q.-L., & Liu, G.-P. (2021). False data injection attacks against partial sensor measurements of networked control systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(1), 149–153.
- Pang, Z.-H., Liu, G.-P., Zhou, D., Hou, F., & Sun, D. (2016). Two-channel false data injection attacks against output tracking control of networked systems. IEEE Transactions on Industrial Electronics, 63(5), 3242–3251. https://doi.org/10.1109/TIE.2016.2535119
- Radhakrishnan, B. M., & Srinivasan, D. (2016). A multi-agent based distributed energy management scheme for smart grid applications. Energy, 103, 192–204. https://doi.org/10.1016/j.energy.2016.02.117
- Rahman, M. S., Mahmud, M. A., Oo, A. M. T., & Pota, H. R. (2016). Multi-agent approach for enhancing security of protection schemes in cyber-physical energy systems. IEEE Transactions on Industrial Informatics, 13(2), 436–447. https://doi.org/10.1109/TII.2016.2612645
- Sargolzaei, A., Yazdani, K., Abbaspour, A., Crane III, C. D., & Dixon, W. E. (2019). Detection and mitigation of false data injection attacks in networked control systems. IEEE Transactions on Industrial Informatics, 16(6), 4281–4292. https://doi.org/10.1109/TII.9424
- Satunin, S., & Babkin, E. (2014). A multi-agent approach to intelligent transportation systems modeling with combinatorial auctions. Expert Systems with Applications, 41(15), 6622–6633. https://doi.org/10.1016/j.eswa.2014.05.015
- Shang, J., Zhou, J., & Chen, T. (2022). Single-dimensional encryption against innovation-based stealthy attacks on remote state estimation. Automatica, 136, 1–16. https://doi.org/10.1016/j.automatica.2021.110015
- Tang, Y., Gao, H., Zhang, W., & Kurths, J. (2015). Leader-following consensus of a class of stochastic delayed multi-agent systems with partial mixed impulses. Automatica, 53, 346–354. https://doi.org/10.1016/j.automatica.2015.01.008
- Wang, T., Hu, M., & Zhao, Y. (2019). Consensus control with a constant gain for discrete-time binary-valued multi-agent systems based on a projected empirical measure method. IEEE/CAA Journal of Automatica Sinica, 6(4), 1052–1059. https://doi.org/10.1109/JAS.6570654
- Xu, M., An, K., Vu, L. H., Ye, Z., Feng, J., & Chen, E. (2019). Optimizing multi-agent based urban traffic signal control system. Journal of Intelligent Transportation Systems, 23(4), 357–369. https://doi.org/10.1080/15472450.2018.1501273
- Yang, W., Zhang, Y., Chen, G., Yang, C., & Shi, L. (2019). Distributed filtering under false data injection attacks. Automatica, 102, 34–44. https://doi.org/10.1016/j.automatica.2018.12.027
- Zeng, Y., Zhang, R., & Lim, T. J. (2016). Wireless communications with unmanned aerial vehicles: Opportunities and challenges. IEEE Communications Magazine, 54(5), 36–42. https://doi.org/10.1109/MCOM.2016.7470933
- Zhang, T.-Y., & Ye, D. (2020). False data injection attacks with complete stealthiness in cyber–physical systems: A self-generated approach. Automatica, 120, 1–14. https://doi.org/10.1016/j.automatica.2020.109117
- Zheng, H., & Shi, D. (2020). A multi-agent system for environmental monitoring using boolean networks and reinforcement learning. Journal of Cybersecurity, 2(2), 85–97.