An Analysis of cyber threats in distributed energy power networks

  • Authors

    • Vijaykumar Kamble Department of Electrical Engineering, AISSMS Institute of Information Technology, Pune, Maharashtra, India
    • Vandana Navale Department of Artificial Intelligence and Data Science, Ajeenkya D. Y. Patil School of Engineering, Pune, Maharashtra, India
    • Varsha Dange Department of Information Technology, Vishwakarma Institute of Technology, Pune, Maharashtra, India
    • Archana Chaudhari Department of Instrumentation Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
    https://doi.org/10.14419/0cz1w646

    Received date: May 11, 2025

    Accepted date: June 3, 2025

    Published date: June 9, 2025

  • Cyberattacks, Vulnerabilities, Microgrid, Distributed System, Denial-of-Service, Communication, Algorithm

  • Abstract

    The power demand has increased dramatically in recent years. Conventional power generation provides about 80% of the world's energy. Distribution networks are essential to the electrical grid system because they link consumers to the transmission system. Distribution networks require careful planning since they are vast and complex. Congestion in the distribution network quickly affects the network's voltage profile as power demand increases, leading to power outages and delayed power delivery. Since Active Distribution Networks (ADNs) are more vulnerable to cyberattacks due to their integration with cutting-edge communication and control technologies, detecting these attacks is a critical problem in modern power systems. The possible cyberattacks on power systems are covered in this paper. To identify the possible, cyberattack, an IEEE-15 bus system with Cyber-Physical Layering (CPL) is suggested. Cyberattack detection systems defend ADNs against assaults including data alteration, illegal access, and service denial by utilizing machine learning, anomaly detection, and real-time monitoring. Using machine learning techniques, such as DT, NNN, and SVM, cyberattack detection is also carried out on modified IEEE-15 bus systems and CPL-based IEEE-15 buses. Additionally, a comparative analysis of the methods for cyberattack detection is conducted.

    The paper discusses how advanced communication and control systems in active distribution networks (ADNs) face heightened cyber risks from both traditional threats like DoS and FDI attacks and emerging quantum computing-based attacks. It highlights vulnerabilities across CPL layers and recommends AI/ML anomaly detection, quantum-safe cryptography, and robust network designs for future resilience.

  • References

    1. T. T. Kim and H. V. Poor, “Strategic Protection Against Data Injection Attacks on Power Grids,” IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 326–333, Jun. 2011, doi: http://dx.doi.org/10.1109/TSG.2011.2119336.
    2. C. Pei, Y. Xiao, W. Liang, and X. Han, “PMU Placement Protection Against Coordinated False Data Injection Attacks in Smart Grid,” IEEE Trans. Ind. Appl., vol. 56, no. 4, pp. 4381–4393, Jul. 2020, doi: http://dx.doi.org/10.1109/TIA.2020.2979793.
    3. L. Cui, Y. Qu, L. Gao, G. Xie, and S. Yu, “Detecting false data attacks using machine learning techniques in smart grid: A survey,” J. Netw. Com-put. Appl., vol. 170, p. 102808, Nov. 2020, doi: http://dx.doi.org/10.1016/j.jnca.2020.102808.
    4. B. M. R. Amin, A. Anwar, and M. J. Hossain, “Distinguishing Between Cyber Injection and Faults Using Machine Learning Algorithms,” in 2018 IEEE Region Ten Symposium (Tensymp), Jul. 2018, pp. 19–24. doi: http://dx.doi.org/10.1109/TENCONSpring.2018.8691899.
    5. M. S. Rahman, M. A. Mahmud, A. M. T. Oo, and H. R. Pota, “Multi-Agent Approach for Enhancing Security of Protection Schemes in Cyber-Physical Energy Systems,” IEEE Trans. Ind. Inform., vol. 13, no. 2, pp. 436–447, Apr. 2017, doi: http://dx.doi.org/10.1109/TII.2016.2612645
    6. B. M. R. Amin, S. Taghizadeh, Md. S. Rahman, Md. J. Hossain, V. Varadharajan, and Z. Chen, “Cyber-attacks in smart grid – dynamic impacts, analyses and recommendations,” IET Cyber-Phys. Syst. Theory Appl., vol. 5, no. 4, pp. 321–329, 2020, doi: http://dx.doi.org/10.1049/iet-cps.2019.0103.
    7. A. A. Jahromi, A. Kemmeugne, D. Kundur, and A. Haddadi, “Cyber-Physical Attacks Targeting Communication-Assisted Protection Schemes,” IEEE Trans. Power Syst., vol. 35, no. 1, pp. 440–450, Jan. 2020, doi: http://dx.doi.org/10.1109/TPWRS.2019.2924441.
    8. X. Liu, M. Shahidehpour, Z. Li, X. Liu, Y. Cao, and Z. Li, “Power System Risk Assessment in Cyber Attacks Considering the Role of Protection Systems,” IEEE Trans. Smart Grid, vol. 8, no. 2, pp. 572–580, Mar. 2017, doi: http://dx.doi.org/10.1109/TSG.2016.2545683.
    9. F. Ünal, A. Almalaq, S. Ekici, and P. Glauner, “Big Data-Driven Detection of False Data Injection Attacks in Smart Meters,” IEEE Access, vol. 9, pp. 144313–144326, 2021, doi: http://dx.doi.org/10.1109/ACCESS.2021.3122009.
    10. L. Wang and A. A. Girgis, “On-line detection of power system small disturbance voltage instability,” IEEE Trans. Power Syst., vol. 11, no. 3, pp. 1304–1313, Aug. 1996, doi: http://dx.doi.org/10.1109/59.535671.
    11. Vijaykumar Kamble, Ajit Bansode, Tushar Waghmare, Kalyan Bamane, “Development of Hybrid Compensators for Enhancing Power Quality in Electrical Distribution Systems Using Innovative Optimization Techniques” International Journal of Smart Grid, Vol.9, No.1, March 2025
    12. P. K. Reddy Shabad, A. Alrashide, and O. Mohammed, “Anomaly Detection in Smart Grids using Machine Learning,” in IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, Oct. 2021, pp. 1–8. doi: http://dx.doi.org/10.1109/IECON48115.2021.9589851.
    13. E. Naderi and A. Asrari, “Toward Detecting Cyberattacks Targeting Modern Power Grids: A Deep Learning Framework,” in 2022 IEEE World AI IoT Congress (AIIoT), Jun. 2022, pp. 357–363. doi: http://dx.doi.org/10.1109/AIIoT54504.2022.9817309.
    14. D. Thukaram, H. P. Khincha, and H. P. Vijaynarasimha, “Artificial neural network and support vector Machine approach for locating faults in radial distribution systems,” IEEE Trans. Power Deliv., vol. 20, no. 2, pp. 710–721, Apr. 2005, doi: http://dx.doi.org/10.1109/TPWRD.2005.844307.
    15. M. Mirzaei, M. Z. A. Ab. Kadir, H. Hizam, and E. Moazami, “Comparative Analysis of Probabilistic Neural Network, Radial Basis Function, and Feed-forward Neural Network for Fault Classification in Power Distribution Systems,” Electr. Power Compon. Syst., vol. 39, no. 16, pp. 1858–1871, Oct. 2011, doi: http://dx.doi.org/10.1080/15325008.2011.615802.
    16. G. B. Gaggero, P. Girdinio, and M. Marchese, “Advancements and Research Trends in Microgrids Cybersecurity,” Appl. Sci., vol. 11, no. 16, Art. no. 16, Jan. 2021, doi: http://dx.doi.org/10.3390/app11167363.
    17. Y. Chen, D. Qi, H. Dong, C. Li, Z. Li, and J. Zhang, “A FDI Attack-Resilient Distributed Secondary Control Strategy for Islanded Microgrids,” IEEE Trans. Smart Grid, vol. 12, no. 3, pp. 1929–1938, May 2021, doi: http://dx.doi.org/10.1109/TSG.2020.3047949.
    18. O. A. Beg, L. V. Nguyen, T. T. Johnson, and A. Davoudi, “Signal Temporal Logic-Based Attack Detection in DC Microgrids,” IEEE Trans. Smart Grid, vol. 10, no. 4, pp. 3585–3595, Jul. 2019, doi: http://dx.doi.org/10.1109/TSG.2018.2832544.
    19. M. S. S. Hakim and H. K. Karegar, “Detection of False Data Injection Attacks Using Cross Wavelet Transform and Machine Learning,” in 2021 11th Smart Grid Conference (SGC), Dec. 2021, pp. 1–5. doi: http://dx.doi.org/10.1109/SGC54087.2021.9664053.
    20. Y. Yang, L. Guo, X. Li, J. Li, W. Liu, and H. He, “A Data-Driven Detection strategy of False Data in Cooperative DC Microgrids,” in IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, Oct. 2021, pp. 1–6. doi: http://dx.doi.org/10.1109/IECON48115.2021.9589318.
    21. S. Ahmed, Y. Lee, S.-H. Hyun, and I. Koo, “Unsupervised Machine Learning-Based Detection of Covert Data Integrity Assault in Smart Grid Networks Utilizing Isolation Forest,” IEEE Trans. Inf. Forensics Secur., vol. 14, no. 10, pp. 2765–2777, Oct. 2019, doi: http://dx.doi.org/10.1109/TIFS.2019.2902822.
    22. A. Kavousi-Fard, W. Su, and T. Jin, “A Machine-Learning-Based Cyber Attack Detection Model for Wireless Sensor Networks in Microgrids,” IEEE Trans. Ind. Inform., vol. 17, no. 1, pp. 650–658, Jan. 2021, doi: http://dx.doi.org/10.1109/TII.2020.2964704.
    23. A. Basati, J. M. Guerrero, J. C. Vasquez, N. Bazmohammadi, and S. Golestan, “A Data-Driven Framework for FDI Attack Detection and Mitiga-tion in DC Microgrids,” Energies, vol. 15, no. 22, Art. no. 22, Jan. 2022, doi: http://dx.doi.org/10.3390/en15228539.
    24. E. Tian, Z. Wu, and X. Xie, “Codesign of FDI Attacks Detection, Isolation, and Mitigation for Complex Microgrid Systems: An HBF-NN-Based Approach,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 5, pp. 6156–6165, May 2024, doi: http://dx.doi.org/10.1109/TNNLS.2022.3230056.
    25. Farhad Zishan, “Investigating the Reliability and Optimal Capacity of Microgrid Electricity Storage Systems with the Aim of Reducing Costs” In-ternational Journal of Smart Grid, Vol.8, No.3, September 2024
    26. Z. S. Warraich and W. G. Morsi, “Early detection of cyber–physical attacks on fast charging stations using machine learning considering vehicle-to-grid operation in microgrids,” Sustain. Energy Grids Netw., vol. 34, p. 101027, Jun. 2023, doi: http://dx.doi.org/10.1016/j.segan.2023.101027.
    27. M. R. Habibi, H. R. Baghaee, F. Blaabjerg, and T. Dragičević, “Secure MPC/ANN-Based False Data Injection Cyber-Attack Detection and Mitiga-tion in DC Microgrids,” IEEE Syst. J., vol. 16, no. 1, pp. 1487–1498, Mar. 2022, doi: http://dx.doi.org/10.1109/JSYST.2021.3086145.
    28. Ehsan Nazemorroaya, Mohsen Shafieirad, Mahdi Majidi, Mahdieh Adeli, “Consensus-Based Algorithm for Distributed Continuous-Time Convex Optimization Over Undirected and Directed Networks” Journal of Applied Research in Electrical Engineering, Vol. 3, No. 1, pp. 74-82, 2024

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  • How to Cite

    Kamble, V., Navale , V. ., Dange , V. ., & Chaudhari, A. . . (2025). An Analysis of cyber threats in distributed energy power networks. International Journal of Basic and Applied Sciences, 14(2), 49-57. https://doi.org/10.14419/0cz1w646