A Multi-Layer Conceptual Security Architecture Toward Robust and Resilient SDN-Enabled Smart Grids

Seref SAGIROGLU, Mehmet Demirci, Yilmaz Atay, Murat Dener, M. Sedef Demirci, Ali Kara, Samed Al, Selcuk Yilmaz

Abstract


Smart grids integrate advanced communication and control technologies to improve the efficiency and reliability of power delivery, but their interconnected and heterogeneous nature expands the attack surface. Recent attacks ranging from malware to false data injection have demonstrated the need for robust, layered defenses spanning physical devices, network infrastructure, control systems, and applications. Software-defined networking (SDN) offers centralized, programmable control over grid communications to dynamically isolate threats and reroute traffic during attacks. Securing SDN-enabled smart grids requires a defense-in-depth approach that combines diverse emerging technologies. This paper proposes a multi-layer conceptual security architecture leveraging blockchain for data integrity, explainable AI for transparent decision-making, generative AI for adaptive threat detection, quantum-safe cryptography for future resilience, big data analytics for anomaly detection, and radio frequency fingerprinting for device authentication. We present our model and discuss how each component contributes to a cohesive security architecture. We also identify open research challenges for integrating these solutions seamlessly to move toward more secure grids in the future.

Keywords


Software-defined networking (SDN), cybersecurity, smart grid, generative AI, radio frequency fingerprinting

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References


S. Demirci and S. Sa??ro?lu, "Software-defined networking for improving security in smart grid systems," in Proc. 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), Paris, France, 2018, pp. 1021–1026, doi: 10.1109/ICRERA.2018.8567005.

Y. Su, P. Jiang, H. Chen, and X. Deng, "A QoS-guaranteed and congestion-controlled SDN routing strategy for smart grid," Applied Sciences, vol. 12, no. 15, p. 7629, Aug. 2022, doi: 10.3390/app12157629.

M. H. Rehmani, A. Davy, B. Jennings, and C. Assi, "Software defined networks-based smart grid communication: a comprehensive survey," IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2637–2670, thirdquarter 2019, doi: 10.1109/COMST.2019.2908266.

D. Kreutz, F. M. V. Ramos, P. E. Veríssimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, "Software-defined networking: a comprehensive survey," Proceedings of the IEEE, vol. 103, no. 1, pp. 14–76, Jan. 2015, doi: 10.1109/JPROC.2014.2371999.

P. Ding, J. Li, L. Wang, M. Wen, and Y. Guan, "HYBRID-CNN: an efficient scheme for abnormal flow detection in the SDN-based smart grid," Security and Communication Networks, vol. 2020, p. 8850550, Aug. 2020, doi: 10.1155/2020/8850550.

S. Demirci, "QoS-Aware Routing Approaches in Software-Defined Smart Grids," in Proc. 9th Int. Conf. Comput. Sci. Eng. (UBMK), 2024, pp. 982–987, doi: 10.1109/UBMK63289.2024.10773500.

F. Hauser, M. Häberle, M. Schmidt, and M. Menth, "A survey on data plane programming with P4: fundamentals, advances, and applied research," Journal of Network and Computer Applications, vol. 212, p. 103561, Mar. 2023, doi: 10.1016/j.jnca.2022.103561.

A. AlSabeh, J. Khoury, E. Kfoury, J. Crichigno, and E. Bou-Harb, "A survey on security applications of P4 programmable switches and a STRIDE-based vulnerability assessment," Computer Networks, vol. 207, p. 108800, Apr. 2022, doi: 10.1016/j.comnet.2022.108800.

M. E. Sahin and M. Demirci, "ConPoolUBF: connection pooling and updatable Bloom filter based SYN flood defense in programmable data planes," Computer Networks, vol. 231, p. 109802, Jul. 2023, doi: 10.1016/j.comnet.2023.109802.

P. Radoglou-Grammatikis, P. Sarigiannidis, T. Lagkas, and I. Moscholios, "SDN-based resilient smart grid: the SDN-microSENSE architecture," Digital, vol. 1, no. 4, pp. 173–187, Nov. 2021, doi: 10.3390/digital1040013.

Y. Y. Ghadi, T. Mazhar, T. Shahzad, M. A. Haq, and E. M. Tag-ElDin, "A hybrid AI-blockchain security framework for smart grids," Scientific Reports, vol. 15, no. 1, p. 20882, Feb. 2025, doi: 10.1038/s41598-025-05257-w.

R. Alsaigh, R. Mehmood, and I. Katib, "AI explainability and governance in smart energy systems: A review," Frontiers in Energy Research, vol. 11, p. 1071291, Feb. 2023, doi: 10.3389/fenrg.2023.1071291.

K. Singh and L. Sevukamoorthy, "Blockchain and AI-based threat detection for enhanced security in financial networks," in Proc. IEEE TEMSCON-ASPAC, Bengaluru, India, 2023, pp. 1–5, doi: 10.1109/TEMSCON-ASPAC59527.2023.10531316.

P. Kumar, R. Kumar, A. Aljuhani, D. Javeed, A. Jolfaei, and A. N. Islam, "Digital twin-driven SDN for smart grid: A deep learning integrated blockchain for cybersecurity," Solar Energy, vol. 263, p. 111921, Oct. 2023, doi: 10.1016/j.solener.2023.111921.

M. J. Islam, A. Rahman, S. Kabir, M. R. Karim, U. K. Acharjee, M. K. Nasir, and S. Wu, "Blockchain-SDN-based energy-aware and distributed secure architecture for IoT in smart cities," IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3850–3864, Mar. 2022, doi: 10.1109/JIOT.2021.3107834.

A. Rahman, M. J. Islam, A. Montieri, M. K. Nasir, M. M. Reza, S. S. Band, and A. Mosavi, "Smartblock-sdn: An optimized blockchain-sdn framework for resource management in IoT," IEEE Access, vol. 9, pp. 28361–28376, Feb. 2021, doi: 10.1109/ACCESS.2021.3058225.

W. Li, Y. Wang, and J. Li, "A blockchain-enabled collaborative intrusion detection framework for SDN-assisted cyber-physical systems," International Journal of Information Security, vol. 22, no. 5, pp. 1219–1230, Oct. 2023, doi: 10.1007/s10207-023-00688-6.

R. Chaudhary, A. Jindal, G. S. Aujla, S. Aggarwal, N. Kumar, and K. K. R. Choo, "BEST: Blockchain-based secure energy trading in SDN-enabled intelligent transportation system," Computers & Security, vol. 85, pp. 288–299, Aug. 2019, doi: 10.1016/j.cose.2019.05.006.

A. Jindal, G. S. Aujla, and N. Kumar, "SURVIVOR: A blockchain based edge-as-a-service framework for secure energy trading in SDN-enabled vehicle-to-grid environment," Computer Networks, vol. 153, pp. 36–48, Apr. 2019, doi: 10.1016/j.comnet.2019.02.012.

U. Ghosh, L. Njilla, S. Shetty, and C. A. Kamhoua, "A decentralized smart grid communication framework using SDN-enabled blockchain," in Proc. 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2024, pp. 982–985, doi: 10.1109/CCNC57856.2024.10454790.

H. Xu, S. Wang, N. Li, K. Wang, Y. Zhao, K. Chen, and H. Wang, "Large language models for cyber security: A systematic literature review," ACM Transactions on Software Engineering and Methodology, vol. 33, no. 6, pp. 1–38, Jul. 2024, doi: 10.1145/3648438.

Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, "Network intrusion detection system: A systematic study of machine learning and deep learning approaches," Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, p. e4150, Jan. 2021, doi: 10.1002/ett.4150.

A. Adadi and M. Berrada, "Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)," IEEE Access, vol. 6, pp. 52138–52160, Sep. 2018, doi: 10.1109/ACCESS.2018.2870052.

J. Gui, Z. Sun, Y. Wen, D. Tao, and J. Ye, "A review on generative adversarial networks: Algorithms, theory, and applications," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3313–3332, Apr. 2023, doi: 10.1109/TKDE.2021.3130191.

M. Ozay, I. Esnaola, F. T. Yarman Vural, S. R. Kulkarni, and H. V. 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, Aug. 2016, doi: 10.1109/TNNLS.2015.2474845.

R. Zhang, H. W. Li, X. Y. Qian, W. B. Jiang, and H. X. Chen, "On large language models safety, security, and privacy: A survey," Journal of Electronic Science and Technology, vol. 23, no. 1, p. 100301, Mar. 2025, doi: 10.1016/j.jnlest.2024.100301.

X. Yan and Y. Ding, "Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices," in Proc. 2nd International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things (FMCPS-IoT '25), May 2025, pp. 19–24, doi: 10.1145/3715015.3725062.

I. Naik, D. Naik, and N. Naik, "Threat Landscape of Adversarial Attacks on Generative AI and Large Language Models (LLMs): Exploring Different Types of Adversarial Attacks, Associated Risks, and Mitigation Strategies," Authorea Preprints, Jan. 2025, doi: 10.22541/au.173711927.42438865/v1.

K. Chahine, M. A. Sayed, A. Al-Ghadhban, and A. Al-Mogren, "Toward secure smart grid systems: risks, threats, challenges, and future directions," Future Internet, vol. 17, no. 7, p. 318, Jul. 2025, doi: 10.3390/fi17070318.

H. Sun, R. Q. Hu, and Y. Qian, "Towards enhancing security for upcoming 6G-ready smart grids: a hierarchical architecture," Cybersecurity, vol. 5, no. 1, p. 14, Dec. 2022, doi: 10.1186/s42400-022-00124-4.

N. Abdulla, S. Demirci, M. Demirci, and S. Özdemir, "Adaptive learning in IoT-based smart city applications," in Principles and Applications of Adaptive Artificial Intelligence, Hershey, PA, USA: IGI Global, 2024, pp. 73–101, doi: 10.4018/979-8-3693-1950-5.ch004.

A. Sarikaya, B. G. K?l?ç, and M. Demirci, "RAIDS: robust autoencoder-based intrusion detection system model against adversarial attacks," Computers & Security, vol. 135, p. 103483, Dec. 2023, doi: 10.1016/j.cose.2023.103483.

M. K. Roshan and A. Zafar, "Boosting robustness of network intrusion detection systems: a novel two phase defense strategy against untargeted white-box optimization adversarial attack," Expert Systems with Applications, vol. 249, p. 123567, Sep. 2024, doi: 10.1016/j.eswa.2024.123567.

Y. Zhu, H. Wen, R. Zhao, Y. Jiang, Q. Liu, and P. Zhang, "Research on data poisoning attack against smart grid cyber–physical system based on edge computing," Sensors, vol. 23, no. 9, p. 4509, May 2023, doi: 10.3390/s23094509.

A. Alajaji, "FortiNIDS: Defending smart city IoT infrastructures against transferable adversarial poisoning in machine learning-based intrusion detection systems," Sensors, vol. 25, no. 19, p. 6056, Oct. 2025, doi: 10.3390/s25196056.

M. T. Naz, W. Elmedany, and M. Ali, "Securing scada systems in smart grids with iot integration: A self-defensive post-quantum blockchain architecture," Internet of Things, vol. 28, p. 101381, Dec. 2024, doi: 10.1016/j.iot.2024.101381.

H. Shekhawat and D. S. Gupta, "A survey on lattice?based security and authentication schemes for smart?grid networks in the post?quantum era," Concurrency and Computation: Practice and Experience, vol. 36, no. 14, p. e8080, Jun. 2024, doi: 10.1002/cpe.8080.

R. Rani, A. K. Singh, A. Parashar, and D. Saxena, "Quantum-Secure Data Transmission in Smart Grid," in Proc. 2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Singapore, 2024, pp. 475–481, doi: 10.1109/MCSoC64094.2024.00078.

N. Skjellum, M. Shaban, and M. Ismail, "SDN-Based Entanglement Routing Strategy in Smart Power Grids Using Reinforcement Learning," in Proc. 2025 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada, 2025, vol. 2, pp. 133–138, doi: 10.1109/QCE61605.2025.10234.

Y. Maleh, Y. Qasmaoui, K. El Gholami, Y. Sadqi, and S. Mounir, "A comprehensive survey on SDN security: threats, mitigations, and future directions," Journal of Reliable Intelligent Environments, vol. 9, no. 2, pp. 201–239, Jun. 2023, doi: 10.1007/s40860-022-00171-8.

A. Jagannath, J. Jagannath, and P. S. P. V. Kumar, "A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges," Computer Networks, vol. 219, p. 109455, Dec. 2022, doi: 10.1016/j.comnet.2022.109455.

J. Zhang, G. Shen, W. Saad, and K. Chowdhury, "Radio frequency fingerprint identification for device authentication in the internet of things," IEEE Communications Magazine, vol. 61, no. 10, pp. 110–115, Oct. 2023, doi: 10.1109/MCOM.003.2200974.

M. A. Awan, Y. Dalveren, F. O. Catak, and A. Kara, "Deployment and implementation aspects of radio frequency fingerprinting in cybersecurity of smart grids," Electronics, vol. 12, no. 24, p. 4914, Dec. 2023, doi: 10.3390/electronics12244914.

M. Safi, S. Dadkhah, F. Shoeleh, H. Mahdikhani, H. Molyneaux, and A. A. Ghorbani, "A survey on IoT profiling, fingerprinting, and identification," ACM Transactions on Internet of Things, vol. 3, no. 4, pp. 1–39, Nov. 2022, doi: 10.1145/3539736.

A. Kara, "Practical implications of the use of radio frequency fingerprinting (RFF) in cybersecurity of smart grids," in Proc. 4th IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0/5.0, Innsbruck, Austria, Feb. 2024, pp. 112–116.




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v10i3.777.g433

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