Taher Alzahrani

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.

This paper proposes a blockchain-anchored artificial intelligence (AI) ambassador framework for trustworthy digital consular services. The design integrates policy-aware retrieval-augmented generation, a permissioned blockchain for tamper-evident evidence commitments, privacy-preserving digital identity with selective disclosure, and governance safeguards for abstention, escalation, prompt and tool control, and red-team evaluation. An extended adoption model links perceived usefulness, ease of use, social influence, facilitating conditions, trust, and perceived risk to behavioral intention and observed use. Using a design science approach, the study performs an ex ante architectural assessment and defines a multi-method protocol for subsequent technical, expert, user, security, and adoption evaluation. The contribution is a traceable architecture and evaluation framework rather than evidence of operational deployment. It provides a foundation for accountable, privacy-aware, and auditable AI-enabled consular e-services while retaining human authority over legal, emergency, and other high-consequence decisions.

This research proposes a fractional-order adaptive neural control scheme using an optimized backstepping (OB) approach to address strict-feedback nonlinear systems with uncertain control directions and predefined performance requirements. The OB framework integrates both fractional-order virtual and actual controllers to achieve global optimization, while a Nussbaum-type function is introduced to handle unknown control paths. To ensure convergence to desired accuracy within a prescribed time, a fractional-order dynamic-switching mechanism and a quartic-barrier Lyapunov function are employed. An input-to-state practically stable (ISpS) auxiliary signal is designed to mitigate unmodeled dynamics, leveraging classical lemmas adapted to fractional-order systems. The study further investigates a decentralized control scenario for large-scale stochastic nonlinear systems with uncertain dynamics, undefined control directions, and unmeasurable states. Fuzzy logic systems are employed to approximate unknown nonlinearities, while a fuzzy-phase observer is designed to estimate inaccessible states. The use of Nussbaum-type functions in decentralized architectures addresses uncertainties in control directions. A key novelty of this work lies in the combination of fractional-order adaptive control, fuzzy logic estimation, and Nussbaum-based decentralized backstepping to guarantee that all closed-loop signals remain bounded in probability. The proposed method ensures that system outputs converge to a small neighborhood around the origin, even under stochastic disturbances. The simulation results confirm the effectiveness and robustness of the proposed control strategy.

The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, a... | Find, read and cite all the research you need on Tech Science Press