Pushpinder Singh Patheja

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

E-mail: pushpinder.singh@vitbhopal.ac.in

Website: https://orcid.org/0000-0003-4252-7735

Research Interests:

Biography

Dr. Pushpinder Singh Patheja is Division Head of Cyber Security and Digital Forensics at the School of Computing Science & Engineering, VIT Bhopal University, India. He has completed his Ph. D. and Post-Graduation from Maulana Azad National Institute of Technology (NIT), Bhopal. He has more than 30 years of experience in different academic and administrative roles in the field of teaching and research. He has more than 50 National and International papers to his credit. He is a member of various research organizations and has a specialization in computer networks, ad hoc networks, cyber security, and network security. Also, He is a Program Evaluator of the National Board of Accreditation. Recently he has Completed the Certification of Certified Ethical Hacker (CEHv10) of EC-Council and is a NASSCOM Certified Trainer for Security Analyst SOC (SSC/Q0909: NVEQF Level 7). He was appointed as an Expert at Smart India Hackathon, 2017 and 2020 organized by MHRD and AICTE, New Delhi.

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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