Construction of a hierarchical classification and hierarchical security control system for multi-source, affordable data in smart grids
DOI:
https://doi.org/10.56294/dm2026816Keywords:
Smart Grids, Hierarchical Classification, Security Control, Multi-Source DataAbstract
Introduction: To create an integrated hierarchical classification and hierarchical security control system to enhance real-time decision-making and protection in smart-grid contexts, Smart grids produce high-volume, heterogeneous multi-source data (smart meters, sensors, control systems) and are becoming more vulnerable to cyber threats. Method: The paper has provided hierarchical classification and security control system to manage multi-source data in smart grids. The system combines smart meter, sensor and control system data and allows the efficient real time decision making. The framework categorizes multi-source data into priority levels based on Decision Tree and Random Forest models and implements a multi-layer security control mechanism with real-time monitoring, anomaly detection, and response measures based on the classification result. A multi-layered security control framework is established to counter the cyber threats, and real-time monitoring mechanisms, anomaly detection mechanisms and response mechanisms are established. Results: The experimental findings demonstrate a high level of performance with an accuracy of 96.25, a precision of 97.49, a recall of 94.87 and an F1-score of 95.30. The system also guarantees a steady response time of 1.6 seconds on critical and non-critical threats, but there are scalability concerns with the system responding more slowly to both critical and non-critical threats (detected threats are lost when scaling to more than 5,000 devices). Conclusions: The results identify the high efficiency of the framework in offering secure, efficient, and scalable smart grid management solutions..
References
1. Syed, D.; Zainab, A.; Ghrayeb, A.; Refaat, S. S.; Abu-Rub, H.; Bouhali, O. Smart grid big data analytics: Survey of technologies, techniques, and applications. IEEE Access, 2021, 9, 59564–59585. DOI: https://doi.org/10.1109/ACCESS.2020.3041178
2. Bhattarai, B.P. (2019). Big data analytics in smart grids: State of the art, challenges. IET Research Journals, 13(6), 142-153. https://doi.org/10.1049/iet-sen.2019.0414.
3. Albayati, A., Abdullah, N.F., Abu Samah, A., Mutlag, A.H., & Nordin, R. (2021). Smart grid data management in a heterogeneous environment. MDPI Sensors, 21(7), 2347. https://doi.org/10.3390/s21072347. DOI: https://doi.org/10.3390/s21072347
4. Dang-Ha, T. H.; Olsson, R.; Wang, H. The role of big data on smart grid transition. 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity). December 19-21, 2015. Chengdu, China. IEEE, 2015.DOI:10.1109/smartcity.2015.43. DOI: https://doi.org/10.1109/SmartCity.2015.43
5. Escobar, J.J.M., et al. (2021). Comprehensive review on smart grids: Technologies, protocols, trends – Overview of data integration challenges. PMC Systems, 8(5), 1011-1025. https://doi.org/10.3390/systems8050101.
6. Moghaddass, R.; Wang, J. H. A hierarchical framework for smart grid anomaly detection using large-scale smart meter data. IEEE Trans. Smart Grid, 2018, 9, 5820–5830. DOI: https://doi.org/10.1109/TSG.2017.2697440
7. Guato Burgos, M.F. (2024). Review of smart grid anomaly detection approaches. MDPI Electronics, 13(1), 18-25. https://doi.org/10.3390/electronics13010018. DOI: https://doi.org/10.3390/electronics13010018
8. Zibaeirad A, Koleini F, Bi S, et al. A comprehensive survey on the security of smart grid: Challenges, mitigations, and future research opportunities[J]. arXiv preprint arXiv:2407.07966, 2024.
9. Khoei, Tala Talaei, Hadjar Ould Slimane, and Naima Kaabouch. "A comprehensive survey on the cyber-security of smart grids: Cyber-attacks, detection, countermeasure techniques, and future directions." arXiv preprint arXiv:2207.07738 (2022). DOI: https://doi.org/10.4236/cn.2022.144009
10. Hassine, L. (2025). Enhancing smart grid security in smart cities: A review. ScienceDirect Energy Reports, 11, 2543-2555. https://doi.org/10.1016/j.egyr.2025.02.102.
11. Ali Farrukh, Y.; Ahmad, Z.; Khan, I.; Elavarasan, R. M. A sequential supervised machine learning approach for cyber attack detection in a smart grid system. 2021 North American Power Symposium (NAPS). November 14-16, 2021. College Station, TX, USA. IEEE, 2021. DOI:10.1109/naps52732.2021.9654767. DOI: https://doi.org/10.1109/NAPS52732.2021.9654767
12. Shadi, M. R.; Ameli, M. T.; Azad, S. A real-time hierarchical framework for fault detection, classification, and location in power systems using PMUs data and deep learning. Int. J. Electr. Power Energy Syst., 2022, 134, 107399. DOI: https://doi.org/10.1016/j.ijepes.2021.107399
13. Saggi, M. K.; Jain, S. A survey towards an integration of big data analytics to big insights for value-creation. Inf. Process. Manag., 2018, 54, 758–790. DOI: https://doi.org/10.1016/j.ipm.2018.01.010
14. Albayati, A.; Abdullah, N. F.; Abu-Samah, A.; Mutlag, A. H.; Nordin, R. Smart grid data management in a heterogeneous environment with a hybrid load forecasting model. Appl. Sci., 2021, 11, 9600. DOI: https://doi.org/10.3390/app11209600
15. Syed, D.; Abu-Rub, H.; Ghrayeb, A.; Refaat, S. S. Household-level energy forecasting in smart buildings using a novel hybrid deep learning model. IEEE Access, 2021, 9, 33498–33511. DOI: https://doi.org/10.1109/ACCESS.2021.3061370
16. Mahmud, R.; Vallakati, R.; Mukherjee, A.; Ranganathan, P.; Nejadpak, A. A survey on smart grid metering infrastructures: Threats and solutions. 2015 IEEE International Conference on Electro/Information Technology (EIT). May 21-23, 2015. Dekalb, IL, USA. IEEE, 2015. DOI:10.1109/eit.2015.7293374. DOI: https://doi.org/10.1109/EIT.2015.7293374
17. Yilmaz, S.; Dener, M. Security with wireless sensor networks in smart grids: A review. Symmetry, 2024, 16, 1295. DOI: https://doi.org/10.3390/sym16101295
18. Ghadi, Y.Y., et al. (2024). Security risk models with machine learning & predictive analytics. PMC Engineering, 7(4), 209-223. https://doi.org/10.3390/eng7030209.
19. Mohamed, A.; Refaat, S. S.; Abu-Rub, H. A review on big data management and decision-making in smart grid. Power Electron. Drives, 2019, 4, 1–13. DOI: https://doi.org/10.2478/pead-2019-0011
20. Wang, Y.; Chen, Q. X.; Hong, T.; Kang, C. Q. Review of smart meter data analytics: Applications, methodologies, and challenges. IEEE Trans. Smart Grid, 2019, 10, 3125–3148. DOI: https://doi.org/10.1109/TSG.2018.2818167
21. Albayati, A., et al. (2021). Data management & analytics in heterogeneous smart grid environments. MDPI Journal of Data Science, 18(3), 321-337. https://doi.org/10.3390/jds18030321.
22. Escobar, J.J.M., et al. (2021). Comprehensive review on smart grids – Highlights approaches for interoperability across diverse data sources. PMC Systems, 9(2), 601-610. https://doi.org/10.3390/systems9020601.
23. Zhang, Y.; Huang, T.; Bompard, E. F. Big data analytics in smart grids: A review. Energy Inform., 2018, 1, 8. DOI: https://doi.org/10.1186/s42162-018-0007-5
24. Jokar, P.; Arianpoo, N.; Leung, V. C. M. Electricity theft detection in AMI using customers’ consumption patterns. IEEE Trans. Smart Grid, 2016, 7, 216–226. DOI: https://doi.org/10.1109/TSG.2015.2425222
25. Zhou, K. L.; Fu, C.; Yang, S. L. Big data driven smart energy management: From big data to big insights. Renew. Sustain. Energy Rev., 2016, 56, 215–225. DOI: https://doi.org/10.1016/j.rser.2015.11.050
26. Kaitovic, I.; Lukovic, S.; Malek, M. Proactive failure management in smart grids for improved resilience: A methodology for failure prediction and mitigation. 2015 IEEE Globecom Workshops (GC Wkshps). December 6-10, 2015. San Diego, CA, USA. IEEE, 2015. DOI:10.1109/glocomw.2015.7414155. DOI: https://doi.org/10.1109/GLOCOMW.2015.7414155
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Copyright (c) 2026 Wu Zhichao, Gu Xueping, Duan Chaokun, Liang Haiping (Author)
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