INTELLIGENT CONTROL AND SECURITY SYSTEMS IN CYBER-PHYSICAL AND CLOUD ENVIRONMENTS OF SMART GRID
DOI:
https://doi.org/10.28925/2663-4023.2025.30.956Keywords:
Smart Grid, cloud computing, edge computing, orchestration, intelligent control, attack resilience, distributed Kalman filter, trust metrics, survivable control, cyber-physical systems.Abstract
The paper examines methodological, algorithmic, and architectural approaches to designing intelligent control and defense systems aimed at ensuring the operational resilience of Smart Grids under the growing intensity of cyber threats. The necessity of transitioning from traditional IT-oriented security tools to hybrid adaptive solutions is substantiated. These solutions combine mechanisms of cyber-physical survivability, automatic recovery, and cognitive response to high-risk attacks such as Denial-of-Service, spoofing, and data injection. A conceptual model of Smart Grid resilience architecture is proposed, built on the synthesis of robust estimation methods, fault-tolerant control, distributed filtering (Distributed Kalman Filtering), and dynamic trust management models. A systematic classification of modern attack vectors targeting digital substations, SCADA, RTU, and HMI components is developed, with formalization of requirements for their protection in the context of both information and physical security. The study explores cloud-edge orchestration of Smart Grid telemetry streams, deploying analytics close to data sources (edge) and scalable aggregation in the cloud for model training and real-time event correlation. The proposed mechanisms account for latency, bandwidth, and workload isolation constraints within cloud environments. The paper highlights challenges in implementing continuous monitoring, anomaly detection, and real-time decision-making, and identifies future directions involving self-learning models for incident detection and reactive control based on artificial intelligence algorithms, considering scalability, reliability, and resilience to hidden threats. The effectiveness of the proposed model is confirmed through experimental simulation of false data injection, DoS, and spoofing attacks in digital substation environments of Smart Grids.
Downloads
References
Sun, C.-C., Liu, C.-C., & Xie, J. (2016). Cyber-physical system security of a power grid: State-of-the-art. Electronics, 5(3), 40. https://doi.org/10.3390/electronics5030040
Younisse, R., & AlKasassbeh, M. (2025). Evaluating deep learning for detecting data integrity attacks in energy smart grids. In 2025 International Conference on New Trends in Computing Sciences (ICTCS), pp. 368–372. https://doi.org/10.1109/ICTCS65341.2025.10989460
Jiang, W., Gao, W., Wang, W., Li, Y., Li, Y., & Zhang, G. (2025). Detection of false data injection attack in smart grid based on extended Kalman and smooth variable structure filter. IEEE Access, 13, 5257–5270. https://doi.org/10.1109/ACCESS.2024.3524558
Zhang, D.-Y., & Li, X.-J. (2025). Fully distributed resilient energy management for smart grids under false data injection attacks. IEEE Internet of Things Journal, 12(15), 32035–32045. https://doi.org/10.1109/JIOT.2025.3575397
Li, L., Ding, S. X., Zhou, L., Zhong, M., & Peng, K. (2024). The system dynamics analysis, resilient and fault-tolerant control for cyber-physical systems. arXiv. https://arxiv.org/abs/2409.13370
Cao, Y., Zhang, L., Zhao, X., Jin, K., & Chen, Z. (2022). An intrusion detection method for industrial control system based on machine learning. Information, 13(7), 322. https://doi.org/10.3390/info13070322
Alonso, M., Turanzas, J., Amaris, H., & Ledo, A. T. (2021). Cyber-physical vulnerability assessment in smart grids based on multilayer complex networks. Sensors, 21(17), 5826. https://doi.org/10.3390/s21175826
Malik, M. I., Ibrahim, A., Hannay, P., & Sikos, L. F. (2023). Developing resilient cyber-physical systems: A review of state-of-the-art malware detection approaches, gaps, and future directions. Computers, 12(4), 79. https://doi.org/10.3390/computers12040079
Kostiuk, Y., Dovzhenko, N., Mazur, N., Skladannyi, P., & Rzaeva, S. (2025). The methodology for protecting grid environments from malicious code during the execution of computational tasks. Cybersecurity: Education, Science, Technique, 3(27), 22–40. https://doi.org/10.28925/2663-4023.2025.27.710
Rashed, M., Gondal, I., Kamruzzaman, J., & Islam, S. (2021). State estimation within IED based smart grid using Kalman estimates. Electronics, 10(15), 1783. https://doi.org/10.3390/electronics10151783
Y. Kostiuk, et al., Architecture of the Software System of Confidential Access to Information Resources of Computer Networks, in: Cyber Security and Data Protection, vol. 4042 (2025) 37-53.
Pei, C., Xiao, Y., Liang, W., & Han, X. (2021). A deviation-based detection method against false data injection attacks in smart grid. IEEE Access, 9, 15499–15509. https://doi.org/10.1109/ACCESS.2021.3051155
Kostiuk, Y., Skladannyi, P., Sokolov, V., Zhyltsov, O., & Ivanichenko, Y. (2025). Effectiveness of information security control using audit logs. In Proceedings of the Workshop on Cybersecurity Providing in Information and Telecommunication Systems (CPITS 2025), pp. 524–538.
Chen, Z., Zhang, H., & Tian, Y. (2023). Security-based resilient distributed energy management against DoS and FDI coordinated attacks. In 2023 35th Chinese Control and Decision Conference (CCDC), pp. 1032–1037. https://doi.org/10.1109/CCDC58219.2023.10326813
P. Skladannyi, et al., Model and Methodology for the Formation of Adaptive Security Profiles for the Protection of Wireless Networks in the Face of Dynamic Cyber Threats, in: Cyber Security and Data Protection, vol. 4042 (2025) 17-36.
Kurt, M. N., Yılmaz, Y., & Wang, X. (2020). Secure distributed dynamic state estimation in wide-area smart grids. IEEE Transactions on Information Forensics and Security, 15, 800–815. https://doi.org/10.1109/TIFS.2019.2928207
Kostiuk, Y., Khorolska, K., Bebeshko, B., Dovzhenko, N., Korshun, N., & Pazynin , A. (2025). Instrumental means of ensuring information security against hidden threats in cloud computing infrastructure. Electronic Professional Scientific Journal «Cybersecurity: Education, Science, Technique», 4(28), 633–655. https://doi.org/10.28925/2663-4023.2025.28.857
Li, X., Wen, M., He, S., Lu, R., & Wang, L. (2024). A privacy-preserving federated learning scheme against poisoning attacks in smart grid. IEEE Internet of Things Journal, 11(9), 16805–16816. https://doi.org/10.1109/JIOT.2024.3365142
Younisse, R., & AlKasassbeh, M. (2024). SGID: A semi-synthetic dataset for injection attacks in smart grid systems. In 2024 15th International Conference on Information and Communication Systems (ICICS), pp. 1–4. https://doi.org/10.1109/ICICS63486.2024.10638278
Y. Kostiuk, et al., Intelligent System for Simulation Modeling and Research of Information Objects, in: 1st Workshop Software Engineering and Semantic Technologies (SEST 2025), vol. 4053 (2025) 237-251.
Johncy, G., & Shaji, R. S. (2024). Secure smart grid implementation with automatic data integrity attack location prediction and exalted energy theft detection. Connection Science, 36(1). https://doi.org/10.1080/09540091.2024.2403393
Kostiuk, Y., Skladannyi, P., Sokolov, V., Hulak, H., & Korshun, N. (2025). Models and algorithms for analyzing information risks during the security audit of personal data information system. In Proceedings of the Third International Conference on Cyber Hygiene & Conflict Management in Global Information Networks (CH&CMiGIN’24), Vol. 3925, pp. 155–171.
An, D., Yang, Q., Liu, W., & Zhang, Y. (2019). Defending against data integrity attacks in smart grid: A deep reinforcement learning-based approach. IEEE Access, 7, 110835–110845. https://doi.org/10.1109/ACCESS.2019.2933020
Kostiuk, Y., Skladannyi, P., Korshun, N., Bebeshko, B., & Khorolska, K. (2024). Integrated protection strategies and adaptive resource distribution for secure video streaming over a Bluetooth network. in: Cybersecurity Providing in Information and Telecommunication Systems II, Vol. 3826, рр.129–138.
Vincent, E., Korki, M., Seyedmahmoudian, M., Stojcevski, A., & Mekhilef, S. (2024). Reinforcement learning-empowered graph convolutional network framework for data integrity attack detection in cyber-physical systems. CSEE Journal of Power and Energy Systems, 10(2), 797–806. https://doi.org/10.17775/CSEEJPES.2023.01250
Kostiuk, Y., Skladannyi, P., Samoilenko, Y., Khorolska, K., Bebeshko, B., & Sokolov, V. (2025). A system for assessing the interdependencies of information system agents in information security risk management using cognitive maps. In Cyber Hygiene & Conflict Management in Global Information Networks, Vol. 3925, pp. 249–264.
Kang, T., Liu, K., Ye, X., Bai, M., Gao, M., & Zhang, W. (2019). Joint modeling and risk simulation analysis based on cyber-physical system in distribution network. In 2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP) (pp. 1754–1758). https://doi.org/10.1109/APAP47170.2019.9224788
Ghiasi, M., Niknam, T., Wang, Z., Mehrandezh, M., Dehghani, M., & Ghadimi, N. (2023). A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future. Electric Power Systems Research, 215, 108975. https://doi.org/10.1016/j.epsr.2022.108975
Dovzhenko, N., Ivanichenko, Y., & Kostiuk, Y. (2025). Graph-based methodology for detection and localization of cyber threats in cloud environments with integrated iot components. Electronic Professional Scientific Journal «Cybersecurity: Education, Science, Technique», 1(29), 762–776. https://doi.org/10.28925/2663-4023.2025.29.938
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Юлія Костюк, Павло Складанний, Світлана Рзаєва, Юлія Самойленко, Наталія Коршун
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
