Adaptive Cyber Defense for Renewable Energy Systems Using Digital Forensics and Fuzzy Multi-Criteria Analysis

As digital technology becomes increasingly integral to modern industries, the risks posed by cyber threats, including malware, ransomware, and insider attacks, continue to rise, jeopardizing critical infrastructure including renewable energy system. The world is more vulnerable to sophisticated cyberattacks due to its reliance on smart grids and IoT-enabled renewable energy systems. Without specialized digital forensic frameworks, incident response and critical infrastructure resilience are limited. This research examines the pivotal role of digital forensics in defending renewable energy system against the growing wave of cyber threats. The study highlights the significance of digital forensics in enhancing incident response, evidence collection, and forensic analysis capabilities. Through detailed case studies, it investigates the implementation strategies of digital forensics to identify, track, and mitigate cyber risks. To address this objective, this study proposes a comprehensive and adaptive cybersecurity framework that integrates digital forensics and fuzzy multi-criteria decision-making to enhance cyber resilience in renewable energy systems. Drawing on relevant case studies, the research demonstrates how the integration of digital forensics with fuzzy logic supports dynamic threat evaluation and risk mitigation. Comparative analysis show that the proposed framework outperforms traditional methods in terms of detection accuracy, response time, and adaptability to evolving threat landscapes. Key contributions include: (1) a structured digital forensics-based cybersecurity model tailored to renewable energy systems, (2) application of fuzzy Analytical Hierarchy Process (AHP) for multi-criteria threat evaluation, and (3) policy-oriented recommendations for stakeholders to reinforce national cyber resilience in line with energy transition. The findings underscore the need for a cohesive cybersecurity strategy grounded in advanced decision-support systems to protect the future of sustainable energy.