Ananya Das

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

E-mail: ananya.d278@gmail.com

Website: https://orcid.org/0009-0004-7300-3189

Research Interests:

Biography

Ananya Das, Graduate Student, School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, India. She is a Women in Cybersecurity member and received their annual WiCyS conference scholarship in 2023. From 2023 to 2024, she interned at INTERPOL Innovation Centre as a Digital Forensic Lab intern at the INTERPOL Global Complex for Innovation, Singapore, and collaboratively published a report on synthetic media and implications on global law enforcement, titled "Beyond Illusions: Unmasking the Threat of Synthetic Media for Law Enforcement".

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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