Work place: School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, Bhopal, Madhya Pradesh 466114, India
E-mail: subhash.patel@vitbhopal.ac.in
Website: https://orcid.org/0000-0002-1136-3638
Research Interests:
Biography
Dr.Subhash Chandra Patel received his PhD degree in CSE from IIT (BHU), Varanasi in 2018 and M.Tech. degree in Information Security from the Guru Gobind Singh Indraprashtha University, New Delhi in 2010. B.Tech in CSE in 2006. Currently he is working as an Senior Assistant Professor in the School of Computer Science Engineering and Artificial Intelligence(SCAI) at VIT University Bhopal. Dr. Subhash Patel has Eight years of Experience in Teaching. He got published various research papers in National and international conferences and Journals also. He reviewed various paper for Transactions on Cloud Computing Journal. His research interests include Cloud Computing Security, Information Security, Internet of Things and Software Engineering.
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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