Author

Nagi Al-shaibany

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Jul 2026

Cybersecurity Governance in the Ministry of Communications and Information Technology in Yemen: Empirical Evidence and a Context-Sensitive Framework

Public sector institutions in developing countries increasingly adopt cybersecurity governance frameworks, yet a persistent gap remains between formal adoption and strategic cybersecurity effectiveness. This study investigates this gap through a case study of the Ministry of Communications and Information Technology in Yemen, guided by ISO/IEC 27014. Using a descriptive-analytical approach, data were collected from 30 cybersecurity decision-makers across six governance dimensions and analyzed using PLS-SEM. The findings reveal a cybersecurity governance maturity gap, as none of the governance dimensions showed a significant impact on strategic effectiveness despite moderate to high implementation levels. Risk assessment and management exhibited a negative, non-significant relationship, indicating symbolic practices. The study proposes a context-sensitive framework to enhance strategic alignment and effectiveness. The findings offer practical guidance for policymakers in developing countries seeking to transition from compliance-oriented cybersecurity governance toward strategically integrated governance frameworks.

Riam Abdulbaset AL-Hakimi AL-Hakimi, Nagi Al-shaibany · مجلة جامعة صنعاء للعلوم التط... · 0 citations

Review Open access Jul 2026

Lightweight Hybrid Deep Learning Models for Real-Time Deepfake Video Detection: A Comprehensive Survey

The rapid advancement of Generative Artificial Intelligence (GAI) has led to the proliferation of deepfake media, posing significant threats to digital security, privacy, and information integrity. To overcome this challenge, substantial research efforts have been directed toward developing automated detection techniques using deep learning methodologies. This study presents a comprehensive survey of deep learning-based deepfake detection methods, emphasizing lightweight and hybrid architectures designed for real-time deployment. The survey systematically categorizes the landscape of deepfake generation techniques and evaluates state-of-the-art detection frameworks, including: classical CNNs, efficient backbone architectures (MobileNet, EfficientNet), and spatiotemporal models (CNN LSTM/GRU). Furthermore, this study examines model compression techniques— including pruning and quantization — essential for resource-constrained deployment, and provides a structured analysis of benchmark datasets, major detection architecture categories, and persistent research gaps. By critically examining the trade-off between detection accuracy and computational latency, this paper identifies key open challenges and concludes by highlighting a research gap for probabilistically robust, lightweight frameworks, offering a roadmap for future research toward reliable, real-time deepfake forensics in unconstrained environments.

Azhar Abdulmughni, Nagi Al-shaibany · مجلة جامعة صنعاء للعلوم التط... · 0 citations