Journal of Cyber Security and Risk Auditing

Journal of Cyber Security and Risk Auditing

ISSN: 3079-5354 (Online)

Publishing model:

: Open access
Scopus Indexed
2025
14.7

CiteScore

Q1
open accessOpen Access

Article

👁️14views

A Risk-Based Cybersecurity Auditing Framework for Smart Grid Infrastructure Using Explainable Artificial Intelligence (XAI)

by 

Udit Mamodiya Orcid link ;

Indra Kishor ;

Hastimal Jangid ;

Rommel AlAli Orcid link ;

Ashraf M. Zaher ;

Shoeb Saleh

PDF logoPDF

Published: 2026/06/27

Abstract

This study suggests a framework for cybersecurity auditing of smart grid infrastructure, which is based on the concept of risk and the use of Explainable Artificial Intelligence (XAI) to produce transparent, prioritized, and audit-ready security evidence. The information from public smart grid cybersecurity events was mapped to event labels, asset classes, security-control status, compliance indicators, and cyber-physical impact variables, which were then used to create audit-relevant records. Attack likelihood estimates were made using machine learning models. The attack likelihood, asset criticality, control deficiency score, compliance condition, and operational impact were all added together to calculate the final audit risk score. Explainability was used as a technique to identify the most important features that affected each audit decision by applying the SHAP method. The proposed framework achieved 96.38% accuracy, 96.51% precision, 96.38% recall, 96.42% F1-score, and 0.996 ROC-AUC. The results of the ablation showed that the inclusion of the risk component and the XAI component resulted in an improvement in the risk ranking, audit traceability, and explanation consistency. The framework translates the cybersecurity detection results into an understandable audit decision, enabling risk-based remediation, compliance review, and understandable smart grid cybersecurity governance.

Keywords

Smart grid cybersecurityrisk-based auditingexplainable artificial intelligencecyber-physical securityaudit risk scoringand SHAP.

References

  1. Pillitteri, V. Y., & Brewer, T. L. (2014). Guidelines for smart grid cybersecurity (NISTIR 7628 Rev. 1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.IR.7628r1
  2. Pascoe, C., Quinn, S., & Scarfone, K. (2024). The NIST Cybersecurity Framework (CSF) 2.0 (NIST Cybersecurity White Paper No. 29). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.CSWP.29
  3. International Society of Automation. (2018). ANSI/ISA-62443-4-2-2018: Security for industrial automation and control systems—Part 4-2: Technical security requirements for IACS components. ISA.
  4. Mamodiya, U., Kishor, I., Naz, R., Almaiah, M., & Alqutaish, A. (2026). A hybrid blockchain-based framework for adaptive cyber-risk prediction and multi-layer threat mitigation in enterprise networks. Journal of Cybersecurity and Privacy, 6(3), 85. https://doi.org/10.3390/jcp6030085
  5. Mukherjee, M., Batabyal, S., Deb Roy, S., Koley, B. L., Debroy, S., & Ray, S. (2025). Deep learning-based fault detection and classification in power distribution networks. Lex Localis, 23(S6), 1915–1928. https://doi.org/10.52152/vdwhe059
  6. Chai, Y. (2025). Research on power system fault diagnosis and prediction model based on deep learning. Advances in Transdisciplinary Engineering. https://doi.org/10.3233/ATDE250838
  7. Zachariades, C., & Xavier, V. (2025). A review of artificial intelligence techniques in fault diagnosis of electric machines. Sensors, 25(16), 5128. https://doi.org/10.3390/s25165128
  8. Ganguly, P., et al. (2025). A machine learning approach to assess the climate change impacts on single and dual-axis tracking photovoltaic systems. Scientific Reports, 15, 24910. https://doi.org/10.1038/s41598-025-10831-3
  9. Attallah, O., Ibrahim, R. A., & Zakzouk, N. E. (2025). A lightweight deep learning framework for transformer fault diagnosis in smart grids using multiple scale CNN features. Scientific Reports, 15, 14505. https://doi.org/10.1038/s41598-025-96290-2
  10. Mamodiya, U., Kishor, I., Pandey, S. K., & Badhan, A. K. (2025). Augmented and virtual reality-driven deep learning for securing critical infrastructures. In Deep Learning Innovations for Securing Critical Infrastructures (pp. 171–182). IGI Global. https://doi.org/10.4018/979-8-3373-0563-9.ch011
  11. Sharma, S. K. (2025). AI-powered cybersecurity: The future of threat detection. Indian Scientific Journal of Research in Engineering and Management, 9(4), 1–9. https://doi.org/10.55041/ijsrem45943
  12. Kishor, I., Almaiah, M., Alqutaish, A., Shehab, R., & Obeidat, M. (2026). Behavior-aware cybersecurity using artificial intelligence and cryptographic intelligence. International Journal of Data and Network Science, 10(2), 699–722.
  13. Rabadan, R., Hussain, A., Simó Mezquita, E., Rodríguez, E., & Masip-Bruin, X. (2025). A machine-learning-based framework for detection and recommendation in response to cyberattacks in critical energy infrastructures. Electronics, 14(15), 2946. https://doi.org/10.3390/electronics14152946
  14. Abdellatif, A., Shaban, K., & Massoud, A. (2024). SDCL: A framework for secure, distributed, and collaborative learning in smart grids. IEEE Internet of Things Magazine, 7, 84–90. https://doi.org/10.1109/IOTM.001.2300059
  15. Uddin, M. S., Sikder, M. S., Anwar, M. M., & Hossain, F. (2025). AI-driven cybersecurity and big data-enabled MIS frameworks: Strengthening supply chain integrity, energy resilience, and critical infrastructure protection. Journal of Computer Science and Technology Studies, 7(9), 223–232. https://doi.org/10.32996/jcsts.2025.7.9.26
  16. Sakkar, U., & Erenoğlu, A. K. (2025). Detection of cyberattacks on photovoltaic systems in smart grid infrastructure using machine learning methods. Fırat University Turkish Journal of Science & Technology, 20(2), 445–454. https://doi.org/10.55525/tjst.1656368
  17. Huang, J., & Wan, Q. (2024). Smart grid line fault detection based on deep learning image recognition algorithm. International Journal of Low-Carbon Technologies, 19, 2174–2180. https://doi.org/10.1093/ijlct/ctae164
  18. Fang, J., Chen, K., Li, C., & He, J. (2023). An explainable and robust method for fault classification and location on transmission lines. IEEE Transactions on Industrial Informatics, 1–10. https://doi.org/10.1109/TII.2022.3229497
  19. Ajayi, O., Mirjafari, M., Idowu, P. B., & Ullah, M. H. (2024). Explainable AI for fault detection and classification in microgrids. In Proceedings of the IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 1835–1840). https://doi.org/10.1109/ECCE55643.2024.10861648
  20. Mamodiya, U., Kishor, I., Mudholkar, P., Alqutaish, A., Alradwan, G., & Obeidat, M. (2026). A robust smart grid-aware cloud computing framework for sustainable energy management. International Journal of Advances in Soft Computing and Its Applications, 18(1), 396–433. https://doi.org/10.15849/ijasca.v18i1.63
  21. Ishfaq, H., Kanwal, S., Anwar, S., Abdussalam, M., & Amin, W. (2025). Enhancing smart grid security and efficiency: AI, energy routing, and T&D innovations (A review). Energies, 18(17), 4747. https://doi.org/10.3390/en18174747
  22. Al-Bermani, N. K., Bermani, A. K., Raad, A., & Manaa, M. E. (2025). AI-driven cybersecurity-based hybrid approach using blockchain for smart grids. Journal of Discrete Mathematical Sciences and Cryptography, 28(4-B), 1399–1411. https://doi.org/10.47974/jdmsc-2286
  23. Arindam, A. (2025). Advancing network security through deep learning: A hybrid graph-based and temporal approach to anomaly and threat detection. International Journal for Science Technology and Engineering, 13(5), 6095–6103. https://doi.org/10.22214/ijraset.2025.71415
  24. Mutambik. (2025). AI-driven cybersecurity in IoT: Adaptive malware detection and lightweight encryption via TRIM-SEC framework. Sensors, 25(22), 7072. https://doi.org/10.3390/s25227072
  25. Mamodiya, U., & Kishor, I. (2026). Artificial intelligence applications for enhancing efficiency in smart grids. In P. Raj, D. P. Sharma, P. K. Dutta, B. S. Prasad, & P. B. Soundarabai (Eds.), Artificial Intelligence (AI) for IT Energy Efficiency and Green AI for Environment Sustainability. Springer. https://doi.org/10.1007/978-3-031-89420-6_9
  26. El Maghraoui, A., El Hadraoui, H., Ledmaoui, Y., El Bazi, N., Guennouni, N., & Chebak, A. (2024). Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability. Sustainable Energy, Grids and Networks, 101452. https://doi.org/10.1016/j.segan.2024.101452
  27. Sarker, M. A. A., Shanmugam, B., Azam, S., & Thennadil, S. (2024). Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability. Intelligent Systems with Applications, 23, 200422. https://doi.org/10.1016/j.iswa.2024.200422
  28. Okeke, O. C., Nwaoha, S. O., & Ezenwegbu, N. C. (2025). Hybrid machine learning models for enhancing cybersecurity in smart grid infrastructures. International Journal of Research and Innovation in Social Science, 4344–4351. https://doi.org/10.47772/IJRISS.2025.90400310
  29. Mamodiya, U., Kishor, I., Vidyullatha, P., Alqutaesh, A., Alradwan, G., & Obeidat, M. (2026). A hybrid fuzzy–deep learning framework for real-time cyber-attack detection in smart energy grids. International Journal of Data and Network Science. https://doi.org/10.5267/j.ijdns.2026.2.007
  30. Duan, J. (2024). Deep learning anomaly detection in AI-powered intelligent power distribution systems. Frontiers in Energy Research, 12, 1364456. https://doi.org/10.3389/fenrg.2024.1364456
  31. Akhtar, I., Atiq, S., Shahid, M. U., Raza, A., Samee, N. A., & Alabdulhafith, M. (2024). Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system. PLOS ONE, 19(8), e0309459. https://doi.org/10.1371/journal.pone.0309459
  32. Ali, N. I., Brohi, I., Jamali, M.-U.-R., Arain, M. B., & Nangraj, A. R. (2025). A revolutionary approach using artificial intelligence and quantum cryptography: A review. International Journal of Innovative Science and Technology. https://doi.org/10.33411/ijist/20257314221436
  33. Wang, B., Baziar, A., & Askari, M. (2025). A deep reinforcement learning framework for adaptive resiliency enhancement in smart power grids. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3593903
  34. Gokulraj, K., & Venkatramanan, C. B. (2024). Advanced machine learning-driven security and anomaly identification in inverter-based cyber-physical microgrids. Electric Power Components and Systems. https://doi.org/10.1080/15325008.2024.2346790
  35. Mohammed, S. H., et al. (2025). Dual-hybrid intrusion detection system to detect false data injection in smart grids. PLOS ONE, 20(1), e0316536. https://doi.org/10.1371/journal.pone.0316536
  36. Rastogi, A., Agrawal, A., Singh, R., & Aggarwal, A. (2024). A comprehensive cybersecurity resilience framework augmenting smart grid stability. In Proceedings of INDISCON (pp. 1–6). https://doi.org/10.1109/INDISCON62179.2024.10744380
  37. Usman, Y., Ihejirika, C. J., Offor, S. N., Robert, A., & Chataut, R. (2025). Green cybersecurity: Leveraging AI, ML, and LLMs to optimize energy, threat detection, and sustainability frameworks. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3602451
  38. Mohammed, A. S., Shohdy, A., Mohammed, S. A., & Montaser, A. M. (2025). Design and optimization of a metamaterial absorber for enhanced solar cell efficiency and wide band microwave cross polarization conversion. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-15840-w
  39. AzithTejaGanti, V. K., Senthilkumar, K., T. L., Karunakaran, S., Pandugula, C., & Khatana, K. (2025). Energy-efficient real-time hybrid deep learning framework for adaptive IoT intrusion detection with scalable and dynamic threat mitigation. Social Science Research Network. https://doi.org/10.2139/ssrn.5077540
  40. Barros, P. H., Agupugo, C. P., Ejichukwu, E., Hayden, M. D., & Ogunmoye, K. A. (2025). Smart grid security: Safeguarding sustainable energy systems from cyber threats. World Journal of Advanced Research and Reviews, 26(3), 1284–1301. https://doi.org/10.30574/wjarr.2025.26.3.2233
  41. Wang, R., Shen, Y., Wang, D., Jiang, Y., & Zhang, C. (2025). DSTF-GKAN: A lightweight spatiotemporal fusion framework for real-time eavesdropping detection in dynamic smart grid networks. PLOS ONE, 20(8), e0330593. https://doi.org/10.1371/journal.pone.0330593
  42. Pandey, T. N., Ravalekar, V., Nair, S. S. K., & Pradhan, S. K. (2025). A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-12515-4
SCImago Journal & Country Rank