An Analytical Study on Cybersecurity Threats and AI-Driven Mitigation Strategies in Next-Generation Smart Grids

Year : 2025 | Volume : 12 | Issue : 03 | Page : 16 25
    By

    Sumit Nagar,

  • Sushma Kakkar,

  • Renu Mishra,

  • Monika Nagar,

  • Sonika Nagar,

  1. Student, Department of Electrical, Electronics and Communication Engineering, Sharda University, Greater Noida, Gautam Budh Nagar, Uttar Pradesh, India
  2. Associate Professor, Department of Electrical, Electronics and Communication Engineering, Sharda University, Greater Noida, Gautam Budh Nagar, Uttar Pradesh, India
  3. Associate Professor, Department of Electrical, Electronics and Communication Engineering, Sharda University, Greater Noida, Gautam Budh Nagar, Uttar Pradesh, India
  4. Assistant Professor, Department of Computer Science, Institute of Management Studies Engineering College, Ghaziabad, Uttar Pradesh, India
  5. Assistant Professor, Academy of Business and Engineering Sciences Engineering College, Ghaziabad, Uttar Pradesh, India

Abstract

The increasing adoption of next-generation smart grids has introduced significant cybersecurity challenges due to their reliance on interconnected digital infrastructures and IoT-based control mechanisms. This study aims to analyze cybersecurity threats in smart grids and explore AI-driven mitigation strategies to enhance grid security and resilience. The research examines common cyber threats such as malware attacks, denial-of-service (DoS), data breaches, and insider threats while evaluating the effectiveness of AI-based solutions, including machine learning (ML), deep learning (DL), and predictive analytics, in real-time threat detection and prevention. The methodology employs a mixed-method approach, integrating historical cyber-incident data, simulated attack scenarios, and performance analysis of AI-driven security frameworks. The findings highlight that AI-enabled solutions significantly improve threat detection accuracy, response time, and system adaptability compared to traditional cybersecurity measures. The study contributes to advancing cybersecurity resilience in smart grids by providing a roadmap for integrating AI into energy infrastructure protection. The results have implications for policymakers, grid operators, and researchers in developing robust and adaptive cybersecurity frameworks for future smart grids.

Keywords: Smart grids, cybersecurity, AI-driven security, machine learning, deep learning, threat mitigation, IoT security, real-time threat detection, cyber threat analysis, critical infrastructure protection

[This article belongs to Journal of Operating Systems Development & Trends ]

How to cite this article:
Sumit Nagar, Sushma Kakkar, Renu Mishra, Monika Nagar, Sonika Nagar. An Analytical Study on Cybersecurity Threats and AI-Driven Mitigation Strategies in Next-Generation Smart Grids. Journal of Operating Systems Development & Trends. 2025; 12(03):16-25.
How to cite this URL:
Sumit Nagar, Sushma Kakkar, Renu Mishra, Monika Nagar, Sonika Nagar. An Analytical Study on Cybersecurity Threats and AI-Driven Mitigation Strategies in Next-Generation Smart Grids. Journal of Operating Systems Development & Trends. 2025; 12(03):16-25. Available from: https://journals.stmjournals.com/joosdt/article=2025/view=232696


References

  1. Faquir D, Chouliaras N, Sofia V, Olga K, Maglaras L. Cybersecurity in smart grids, challenges and solutions. AIMS Electron Electr Eng. 2021 Jan; 5(1): 24–37.
  2. Sikha VK, Somepalli S. Cybersecurity in Utilities: Protecting Critical Infrastructure from Emerging Threats. J Sci Eng Res. 2023; 10(12): 233–42.
  3. Lamba V, Šimková N, Rossi B. Recommendations for smart grid security risk management. Cyber-Phys Syst. 2019 Apr 3; 5(2): 92–118.
  4. Ruan J, Liang G, Zhao J, Zhao H, Qiu J, Wen F, Dong ZY. Deep learning for cybersecurity in smart grids: Review and perspectives. Energy Convers Econ. 2023 Aug; 4(4): 233–51.
  5. Huseinović A, Mrdović S, Bicakci K, Uludag S. A survey of denial-of-service attacks and solutions in the smart grid. IEEE Access. 2020 Sep 25; 8: 177447–70.
  6. Moreno Escobar JJ, Morales Matamoros O, Tejeida Padilla R, Lina Reyes I, Quintana Espinosa H. A comprehensive review on smart grids: Challenges and opportunities. Sensors. 2021 Oct 21; 21(21): 6978.
  7. Choudhury NR, Paul S. Comparative analysis of traditional vs. AI-driven network security. In: Kumar R, Srivastava S, Elngar AA, editors. Human Impact on Security and Privacy: Network and Human Security, Social Media, and Devices. Hershey (PA): IGI Global Scientific Publishing; 2025. p. 53–74. doi: 10.4018/979-8-3693-9235-5.ch004.
  8. Sewak M, Sahay SK, Rathore H. Deep reinforcement learning in the advanced cybersecurity threat detection and protection. Inf Syst Front. 2023 Apr; 25(2): 589–611.
  9. Ruan J, Liang G, Zhao J, Zhao H, Qiu J, Wen F, Dong ZY. Deep learning for cybersecurity in smart grids: Review and perspectives. Energy Convers Econ. 2023 Aug; 4(4): 233–51.
  10. Bhuiyan T. AI in Smart Grid Cybersecurity: A Systematic Review of Machine Learning and Deep Learning Approaches against False Data Injection and Other Emerging Attacks. J Comput Sci Technol Stud. 2025 Aug 25; 7(8): 1207–95.
  11. Campbell RJ. The smart grid and cybersecurity: Regulatory policy and issues. Curr Polit Econ US Can Mex. 2017 Apr 1; 19(2): 169–200.
  12. Roberts A. AI-Driven Threat Intelligence for Proactive Cybersecurity in Smart Grid Systems. Int J AI Big Data Comput Manag Stud. 2024 Mar 20; 5(1): 26–34.
  13. Parisa SK, Banerjee S. AI-Enabled Cloud Security Solutions: A Comparative Review of Traditional vs. Next-Generation Approaches. Int J Stat Comput Simul. 2024 Jan 16; 16(1).
  14. Amanlou S, Hasan MK, Mokhtar UA, Malik KM, Islam S, Khan S, Khan MA, Khan MA. Cybersecurity challenges in smart grid systems: current and emerging attacks, opportunities, and recommendations. IEEE Open J Commun Soc. 2025 Feb 25; 6: 1965–1997.
  15. Ribas Monteiro LF, Rodrigues YR, Zambroni de Souza AC. Cybersecurity in cyber–physical power systems. Energies. 2023 Jun 7; 16(12): 4556.
  16. Chen D, Lin X, Qiao Y. Perspectives for artificial intelligence in sustainable energy systems. Energy. 2025 Mar 1; 318: 134711.
  17. Li XJ, Ma M, Sun Y. An adaptive deep learning neural network model to enhance machine-learning-based classifiers for intrusion detection in smart grids. Algorithms. 2023 Jun 2; 16(6): 288.
  18. Wang Y, Chen CF, Kong PY, Li H, Wen Q. A cyber–physical–social perspective on future smart distribution systems. Proc IEEE. 2022 Jul 28; 111(7): 694–724.
  19. Khan AA, Laghari AA, Rashid M, Li H, Javed AR, Gadekallu TR. Artificial intelligence and blockchain technology for secure smart grid and power distribution Automation: A State-of-the-Art Review. Sustain Energy Technol Assess. 2023 Jun 1; 57: 103282.
  20. Su Z, Wang Y, Luan TH, Zhang N, Li F, Chen T, Cao H. Secure and efficient federated learning for smart grid with edge-cloud collaboration. IEEE Trans Ind Inform. 2021 Jul 8; 18(2): 1333–44.
Regular Issue Subscription Original Research
Volume 12
Issue 03
Received 08/09/2025
Accepted 11/10/2025
Published 19/11/2025
Publication Time 72 Days
59

Login


My IP

PlumX Metrics