Autonomous Cyber Defense for Resilient Power Grids Using Explainable Artificial Intelligence and SCADA Intelligence

Tunbosun Ajibola and Komeno Okpiyalele

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 3, 938-958

Abstract: The digital convergence of supervisory control and data acquisition (SCADA), operational technology, phasor measurement, and IP-based communications has increased the observability and controllability of modern power grids while enlarging their cyberattack surface. This paper develops an autonomous cyber-defense architecture that combines heterogeneous SCADA intelligence, explainable artificial intelligence (XAI), and safety-constrained response selection to detect, interpret, and contain cyber-physical threats without sacrificing grid continuity. The experimental dataset supplied for the study contains 240,000 labeled records: 120,000 normal observations and 120,000 attack observations spanning false data injection, denial-of-service/distributed denial-of-service, replay, command injection, reconnaissance, and relay/device manipulation. A 70:15:15 train-validation-test partition is used with cyber, physical, protocol, temporal, and security-context features. Across the supplied repeated-run ranges, the proposed XAI-SCADA fusion model reports 96-98% accuracy, recall, and F1-score, with a central F1 value of 97%, compared with 95% for a CNN/LSTM baseline, 93.5% for Random Forest, 92% for Support Vector Machine, and 85.5% for the signature/rule baseline when the tabulated ranges are used consistently. Attack-specific recall ranges from 93-99%. SHAP analysis identifies frequency deviation, voltage phase angle, anomalous control commands, current magnitude, and breaker-state mismatch as the strongest explanatory drivers. The supplied response timing sums to 1.40 s from detection through containment, while the recovery trajectory restores 96% of critical load within 10 s and 100% by 30 s. A normalized service-resilience index of 0.955 is obtained for autonomous defense versus 0.8875 for detection-only defense. The results support a move from passive intrusion detection toward interpretable, operationally constrained, and resilience-aware cyber defense for critical power infrastructure.

Keywords: autonomous cyber defense; explainable artificial intelligence; SCADA; smart grid cybersecurity; cyber-physical systems; false data injection; grid resilience; operational technology (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT261242123
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DOI: 10.32628/CSEIT261242123

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