Global Threat Intelligence Network to Collect and Display Terrorist Profiles at a Single Platform

Abstract

This project presents the development of a centralized, web-based platform titled Global Terrorism Database, aimed at compiling and providing comprehensive information on known terrorists across the world. The website serves as a unified intelligence interface where verified data—including names, photographs, affiliations, criminal history, and bounty details—are organized and made accessible to both security agencies and the general public. A key feature of the platform is its public reporting system, which allows individuals to submit tips, sightings, or relevant information regarding the listed terrorists. These reports are securely forwarded to concerned law enforcement or intelligence agencies. In cases where a reward is officially declared for information on a particular terrorist, the platform enables a transparent system for informers to claim such incentives, creating a mutual benefit model for both agencies and civilians. By reducing duplication of effort across nations and encouraging crowd-sourced intelligence, the website seeks to support global counter-terrorism initiatives through better coordination, public participation, and rapid information exchange. This project integrates technology with security efforts, offering an innovative tool in the fight against terrorism.

Introduction

The Global Threat Intelligence Network (GTIN) is a conceptual web-based platform designed to centralize profiles of globally wanted terrorists and facilitate public engagement in counter-terrorism. Amid the rise of transnational extremism, there is a critical gap in publicly accessible, real-time intelligence. GTIN addresses this by consolidating detailed profiles—including names, aliases, organizational affiliations, charges, last known locations, and bounties—into a searchable, user-friendly interface.

The platform incorporates features such as keyword search, regional filters, and “CLASSIFIED” overlays to simulate real-world intelligence access controls. A key innovation is a public reporting mechanism that allows civilians to submit sightings or intelligence leads, potentially earning rewards, thereby fostering collaborative intelligence sharing. Currently implemented as a static front-end prototype, GTIN is modular and scalable, enabling future integration with backend databases, AI-based threat analytics, geospatial mapping, and authentication systems.

The literature review highlights gaps in existing resources: databases like the Global Terrorism Database provide historical data but lack real-time interactivity, while FBI and INTERPOL lists are regionally constrained and non-interactive. Crowd-sourced intelligence and open-source platforms have potential but require secure frameworks to avoid misuse.

The implementation demonstrates core functionality: a search engine, filterable profile cards, and simulated intelligence tools. While the prototype is limited by static data and lacks encryption or AI features, it shows feasibility for centralized intelligence access, civilian reporting channels, and scalable architecture for secure deployment.

In conclusion, GTIN exemplifies a forward-looking approach to counter-terrorism, combining centralized intelligence, public engagement, and extensible digital design to bridge the gap between government agencies and civilians in a secure, collaborative environment.

Conclusion

The Global Threat Intelligence Network (GTIN) represents a forward-thinking approach to digital counter-terrorism infrastructure by consolidating decentralized intelligence into a single, interactive, and civilian-accessible platform. Through the development of a dynamic, searchable interface that simulates real-world intelligence dashboards, the project demonstrates the technical feasibility of integrating public reporting systems with structured terrorist profiling. The design successfully models essential functionalities such as keyword filtering, profile-based visualization, and access-restricted data presentation. While currently implemented as a front-end prototype with static content, the system architecture is purposefully modular—enabling seamless transition to a fully integrated, database-driven, and secure reporting environment. Importantly, the project introduces a participatory intelligence model wherein civilians can contribute information in exchange for potential rewards, fostering a cooperative ecosystem between agencies and the public. In doing so, GTIN addresses existing gaps in transparency, accessibility, and real-time responsiveness found in traditional threat monitoring systems. This work lays the conceptual and technical foundation for future development of scalable, secure, and ethically responsible public-facing counter-terrorism platforms.

References

[1] Eldridge, C., Hobbs, C., & Moran, M. (2018). Fusing algorithms and analysts: open-source intelligence in the age of ‘Big Data’. Intelligence and National Security, 33(3), 391-406. [2] Chaudhary, M., & Bansal, D. (2022). Open source intelligence extraction for terrorism?related information: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(5), e1473. [3] Weimann, G. (2015). Terrorism in cyberspace: The next generation. Columbia University Press. [4] Hatfield, J. M. (2024). There is no such thing as open source intelligence. International journal of intelligence and CounterIntelligence, 37(2), 397-418. [5] Mashechkin, I. V., Petrovskiy, M. I., Tsarev, D. V., & Chikunov, M. N. (2019). Machine learning methods for detecting and monitoring extremist information on the internet. Programming and Computer Software, 45(3), 99-115. [6] Huamaní, E. L., Alva, M. A., & Roman-Gonzalez, A. (2020). Machine learning techniques to visualize and predict terrorist attacks worldwide using the global terrorism database. International Journal of Advanced Computer Science and Applications, 11(4). [7] Baby, A., & Sruthi, A. (2023, November). Machine learning models for prediction of terrorist attacks: A comparative analysis in terrorism prone regions. In 2023 Annual international conference on emerging research areas: International conference on intelligent systems (AICERA/ICIS) (pp. 1-6). IEEE. [8] LaFree, G., & Gill, P. (2024). Strengths and weaknesses of open source data for studying terrorism and political radicalization. Studies in Conflict & Terrorism, 1-17. [9] Renard, T., & Demuynck, M. (2025). Migration-related Terrorism: Trends, Challenges, and Policy Implications. [10] Index, G. T. (2020). Global Terrorism Index: Measuring the impact of terrorism. Institute for Economics and Peace. Retrieved June, 29, 2022. [11] Chen, H., Reid, E., Sinai, J., Silke, A., & Ganor, B. (Eds.). (2008). Terrorism informatics: Knowledge management and data mining for homeland security (Vol. 18). Springer Science & Business Media. [12] Chaudhary, M., Vashistha, S., & Bansal, D. (2022). Automated detection of anti-national textual response to terroristic events on online media. Cybernetics and Systems, 53(8), 702-715. [13] Iliou, C., Tsikrika, T., Vrochidis, S., & Kompatsiaris, Y. (2017, February). Evasive focused crawling by exploiting human browsing behaviour: a study on terrorism-related content. In Proceedings of the 1st International Workshop on Cyber Deviance Detection co-located with the Tenth International Conference on Web Search and Data Mining CyberDD@ WSDM. [14] Adel, A., & Norouzifard, M. (2024). Weaponization of the growing cybercrimes inside the dark net: The question of detection and application. Big Data and Cognitive Computing, 8(8), 91. [15] Conway, M., Jarvis, L., & Lehane, O. (Eds.). (2017). Terrorists’ use of the Internet: Assessment and response (Vol. 136). Ios Press. [16] Tinnes, J. (2024). Risk Assessment of Terrorism. Perspectives on Terrorism, 18(1), 144-207. [17] Pai, Y., & Krishna Prasad, K. Open Source Intelligence and its Applications in Next Generation Cyber Security-A. [18] Bjelopera, J. P. (2013). American jihadist terrorism: Combating a complex threat. DIANE Publishing. [19] Singh, A. K., Ashok/Ali Siddiqui Kumar Singh (Zeesha), & Singh, S. (2024). Recent advances in computational intelligence and cyber security.3

Copyright

Copyright © 2025 Bhavya Sharma, Abhinand T, Kalidindi Sowmya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.