Azath H.

Work place: School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, Bhopal, Madhya Pradesh 466114, India

E-mail: writetoazath@yahoo.com

Website: https://orcid.org/0000-0002-7734-7745

Research Interests:

Biography

Dr. H. AZATH, Associate Professor / CSE, Division Head / Cyber Security & Digital Forensics, VIT Bhopal University, INDIA. Dr. H. Azath received his degrees Ph.D, ME, BE in the field of Computer Science & Engineering and M.TECH-IT, MBA in Education Management. He has 20+ years of academic experience under various capacities in India and abroad. He is a reviewer for two international journals of SCI indexed. He has also been a reviewer in International Conference at University of Missourie (UMKC), Kansas City, US. Dr. Azath has 13 patents to his credit and 40 publications in SCI & Scopus indexed journals. He has also authored 4 books and 4 book chapters. His areas of interest are Cyber Security, Secure Software Engineering, Artificial Intelligence, Networking. He has collaborated with a number of International faculty in Academics and Research and is a Member of ISTE since 2005.

Author Articles
Integration of Cyber Threat Hunting and Socio- Political Risk Assessment for a Hybrid Predictive Model for Enhanced Global Security

By Ananya Das Azath H. Subhash Chandra Patel Pushpinder Singh Patheja

DOI: https://doi.org/10.5815/ijwmt.2025.05.02, Pub. Date: 8 Oct. 2025

The new and emerging challenges posed by the convergence of cyber threats and socio-political tensions have risen as one of the core formidable threats to the present global security landscape. This paper proposes a hybrid predictive model intended to act against these real-world multidimensional attack vectors. The model integrates cyber threat hunting techniques with socio-political risk assessment methodologies to comprehensively forecast consequent cybersecurity threats to social unrest scenarios. Cyber threat data is collected from sources such as the Offensive Defensive-Intrusion Detection System (OD-IDS2022) and the Aegean Wi-Fi Intrusion Dataset (AWID3), and social terror attack information is gathered from the Global Database of Events, Language, and Tone (GDLET) Project and Armed Conflict Location & Event Data (ACLED) to comprise the bidirectional dataset for the model that contains views from both cyber and socio-political risk landscapes. The model adopts a holistic, robust predictive capability through k-fold cross-validation and feature importance evaluation implementation techniques. This multidisciplinary approach offers a synoptic understanding of emerging and future security threats and enables the execution of proactive measures to secure national and transnational borders.

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