[Paper] An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids

Source: arXiv

Abstract

Smart grids are a fusion of classical power infrastructure and advanced communication networks and smart control, to create a cyber‑physical environment that is more efficient and flexible than ever before. This integration causes vulnerabilities that can undermine grid stability as well as reliability. Digital forensics is a fundamental concept of learning and identifying, detecting, and mitigating such security incidents. This paper presents an all‑in‑one machine‑learning‑based digital forensic framework of smart‑grid systems deployed on the Cloud. The framework combines data acquisition at the sensor level, authenticated communication, scalable cloud storage, and automated forensic analytics. The model uses supervised and unsupervised learning algorithms—such as Random Forest, Support Vector Machine, Gradient Boosted Trees, and deep neural architectures—for anomaly detection, event reconstruction, and intrusion analysis in real time. After several simulation and experimental studies on real‑time smart‑meter data streams, the proposed framework is shown to be very accurate, scalable, and resilient to cyber‑attacks including data tampering, false‑data injection, and coordinated control‑loop manipulation. The results indicate that cloud services are the best backbone for big‑data‑driven forensic workflows, which allows energy utilities to achieve fast situational awareness and intelligent incident response.

Metadata

• Subjects: Machine Learning (cs.LG)
• Cite as: arXiv:2511.21590
• DOI: https://doi.org/10.48550/arXiv.2511.21590
• Submission history: v1, Wed, 26 Nov 2025 17:08:06 UTC (5,000 KB)