AI-driven cybersecurity-based hybrid approach using blockchain for smart grids
*Noora Kadhim Al-BermaniCorresponding authormat.noorakadhim@uobabylon.edu.iqDepartment of Ceramic and Building Materials EngineeringCollege of Materials EngineeringUniversity of BabylonBabylon, IraqView full profile → , Ali Kadhim Bermaniali.k.bermani@uomus.edu.iqAffiliation 1Department of NetworkCollege of Information TechnologyUniversity of BabylonBabylon, IraqAffiliation 2Department of Computer Techniques EngineeringCollege of Engineering and TechnologiesAl-Mustaqbal UniversityBabylon, IraqView full profile → , Abeer Raadabeer.hassan.ims@atu.edu.iqDepartment of Medical Instruments TechniquesTechnical Institute of Al-MussaibAl-Furat Al-Awsat Technical UniversityBabylon, IraqView full profile → , Mehdi Ebady Manaamahdi.ebadi@uomus.edu.iqAffiliation 1Department of NetworkCollege of Information TechnologyUniversity of BabylonBabylon, IraqAffiliation 2Department of Intelligent Medical SystemCollege of SciencesAl-Mustaqbal UniversityBabylon, IraqView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 04 Feb 2025
- Published Online:
- 26 Jun 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2286
- Pages:
- 1399–1411
Abstract
Keywords
Subject Classifications
References
[1] P. Rani, S. Verma, S. P. Yadav, B. K. Rai, M. S. Naruka, and D. Kumar, “Simulation of the lightweight blockchain technique based on privacy and security for healthcare data for the cloud system,” Int. J. E-Health Med. Commun. (IJEHMC), vol. 13, no. 4, pp. 1–15 (2022).
[2] P. Rani, M. S. Naruka, B. K. Rai, D. Kumar, S. Verma, and S. P. Yadav, “Federated learning-based misbehaviour detection for the 5G-enabled Internet of Vehicles,” IEEE Trans. Consum. Electron. (2023).
[3] V. Vahidinasab, S. Nikkhah, A. Allahham, and D. Giaouris, “Boosting integration capacity of electric vehicles: A robust security constrained decision making,” Int. J. Electr. Power Energy Syst., vol. 133, p. 107229 (Dec. 2021), doi: 10.1016/j.ijepes.2021.107229.
[4] A. Allahham, D. Greenwood, C. Patsios, and P. Taylor, “Adaptive receding horizon control for battery energy storage management with age-and-operation-dependent efficiency and degradation,” Electr. Power Syst. Res., vol. 209, p. 107936 (Aug. 2022), doi: 10.1016/j.epsr.2022.107936.
[5] N. Hussain and P. Rani, “Comparative studied based on attack resilient and efficient protocol with intrusion detection system based on deep neural network for vehicular system security,” in Distributed Artificial Intelligence, CRC Press, pp. 217–236 (2020).
[6] S. Nikkhah, A. Alahyari, A. Allahham, and K. Alawasa, “Optimal integration of hybrid energy systems: A security-constrained network topology reconfiguration,” Energies, vol. 16, no. 6, p. 2780 (Mar. 2023), doi: 10.3390/en16062780.
[7] A. Kumari and S. Tanwar, “A data analytics scheme for security-aware demand response management in smart grid system,” in Proc. 2020 IEEE 7th UPCON, Prayagraj, India, pp. 1–6 (Nov. 2020), doi: 10.1109/UPCON50219.2020.9376458.
[8] A. Kumari and S. Tanwar, “A reinforcement-learning-based secure demand response scheme for smart grid system,” IEEE Internet Things J., vol. 9, no. 3, pp. 2180–2191 (Feb. 2022), doi: 10.1109/JIOT.2021.3090305.
[9] N. Oliveira, I. Praça, E. Maia, and O. Sousa, “Intelligent cyber attack detection and classification for network-based intrusion detection systems,” Appl. Sci., vol. 11, no. 4, p. 1674 (Feb. 2021), doi: 10.3390/app11041674.
[10] K. Manandhar, X. Cao, F. Hu, and Y. Liu, “Detection of faults and attacks including false data injection attack in smart grid using Kalman filter,” IEEE Trans. Control Netw. Syst., vol. 1, no. 4, pp. 370–379 (Dec. 2014), doi: 10.1109/TCNS.2014.2357531.
[11] P. Rani, U. C. Garjola, and H. Abbas, “A predictive IoT and cloud framework for smart healthcare monitoring using integrated deep learning model,” NJF Intell. Eng. J., vol. 1, no. 1, pp. 53–65 (2024).
[12] N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” in Proc. 2015 MilCIS, Canberra, Australia: IEEE, pp. 1–6 (2015). [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7348942/
[13] D. Lin, J. Wu, Q. Yuan, and Z. Zheng, “Modeling and understanding Ethereum transaction records via a complex network approach,” IEEE Trans. Circuits Syst. II: Express Briefs, vol. 67, no. 11, pp. 2737–2741 (2020).
[14] A. S. Bozkir, F. C. Dalgic, and M. Aydos, “GramBeddings: A new neural network for URL-based identification of phishing web pages through n-gram embeddings,” Comput. Secur., vol. 124, p. 102964 (2023).
[15] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, “African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems,” Comput. Ind. Eng., vol. 158, p. 107408 (2021).
[16] T. S. Ustun, S. S. Hussain, A. Ulutas, A. Onen, M. M. Roomi, and D. Mashima, “Machine learning-based intrusion detection for achieving cybersecurity in smart grids using IEC 61850 GOOSE messages,” Symmetry, vol. 13, no. 5, p. 826 (2021).
[17] M. Ghiasi, M. Dehghani, T. Niknam, A. Kavousi-Fard, P. Siano, and H. H. Alhelou, “Cyber-attack detection and cyber-security enhancement in smart DC-microgrid based on blockchain technology and Hilbert–Huang transform,” IEEE Access, vol. 9, pp. 29429–29440 (2021).
[18] X. Zhu, W. Wang, S. Pang, C. An, X. Yang, and Y. Wu, “Blockchain-based optimal decision making of dispatchable units in smart grids considering the high uncertainty effects,” Sustain. Cities Soc., vol. 76, p. 103418 (2022).
[19] A. S. Abdulbaqi, I. F. Muslim, A. A. A. Al-Ameer, and A. J. Obaid, “Healthcare surveillance based on cloud computing utilizing mobile devices,” J. Discrete Math. Sci. Cryptogr., pp. 1–8, [Online]. doi: 10.47974/JDMSC-1566.
