Counter Terrorism Prediction and Risk Evaluation (C-TRIP)

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Year : 2024 | Volume :02 | Issue : 02 | Page : –
By
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Yash Shirsath,

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Vedant Bhosale,

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Manoj Deshpande,

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Shilpali Bansu,

  1. Student, Department of Artificial Intelligence & Data Science, Jawahar Education Society’s AC Patil College of Engineering, Kharghar, Navi Mumbai, Maharashtra, India
  2. Student, Department of Artificial Intelligence & Data Science, Jawahar Education Society’s AC Patil College of Engineering, Kharghar, Navi Mumbai, Maharashtra, India
  3. Head & Professor, Department of Computer Engineering, Jawahar Education Society’s AC Patil College of Engineering, Kharghar, Navi Mumbai, Maharashtra, India
  4. HOD and Assistant Professor, Department of Artificial Intelligence & Data Science, Jawahar Education Society’s AC Patil College of Engineering, Kharghar, Navi Mumbai, Maharashtra, India

Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_109009’);});Edit Abstract & Keyword

The global landscape in the 21st century is marked by complex and evolving security challenges, none more pressing than the threat of terrorism. Acts of terror have left a profound impact on societies, economies, and governments worldwide, underscoring the critical importance of effective counter-terrorism strategies. The “Counter Terrorism Prediction and Risk Evaluation (C-TRIP)” represents a significant stride in addressing this ever-pressing challenge. In a time marked by global security challenges, “Counter Terrorism Prediction and Risk Evaluation (C-TRIP)” is emerging as an innovative solution to deal with the ever-evolving Terrorist threats. The project arises from the pressing need for predictive systems. It can anticipate and mitigate the possibility of terrorism. The motivation is external This effort is reflected in the rise and proliferation of terrorist incidents dramatically in recent years. Traditional counter terrorism response strategies are not the right length of time. Timely, data-driven interventions are needed to address this matter of great importance. This offers an extensive overview of the current literature. Critically examines the strengths and limitations of past research. Define what is important the research gap in the current work, and the search for a new framework, was emphasized which can provide an accurate risk assessment and early warning. It employs a data-driven methodology utilizing machine learning and data analysis techniques to predict and assess terrorist incidents. C-TRIP aims to enhance decision-making in counter-terrorism efforts by providing timely and accurate insights into the likelihood and severity of potential terrorist events.

Keywords: Counter-terrorism, Terrorism, Security challenges, Global landscape, Predictive systems, Data-driven interventions, Global security, Predictive systems, Threat assessment, Early warning, Decision-making, Methodology, Architecture, Experimental results

[This article belongs to International Journal of Information Security Engineering (ijise)]

How to cite this article:
Yash Shirsath, Vedant Bhosale, Manoj Deshpande, Shilpali Bansu. Counter Terrorism Prediction and Risk Evaluation (C-TRIP). International Journal of Information Security Engineering. 2024; 02(02):-.
How to cite this URL:
Yash Shirsath, Vedant Bhosale, Manoj Deshpande, Shilpali Bansu. Counter Terrorism Prediction and Risk Evaluation (C-TRIP). International Journal of Information Security Engineering. 2024; 02(02):-. Available from: https://journals.stmjournals.com/ijise/article=2024/view=0

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References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_109009’);});Edit

  1. A Look Back at 2020: Trends from the Global Terrorism Database (GTD)TM | START.umd.edu. Umd.edu. 2022. Available from: https://www.start.umd.edu/look-back-2020-trends-global-terrorism-database-gtd ‌
  2. DHS/ALL/PIA-084 Joint-Threat Information Management System (J-TIMS) | Homeland Security. U.S. Department of Homeland Security. 2024. Available from: https://www.dhs.gov/publication/ dhsallpia-084-joint-threat-information-management-system-j-tims
  3. Team I. Financing Violent Extremism: An Examination of Maligned Creativity in the Use of Financial Technologies-ICSR. 2023. Available from: https://icsr.info/2023/04/12/financing-violent-extremism-an-examination-of-maligned-creativity-in-the-use-of-financial-technologies/ ‌
  4. Publicly accessible locations (PALs) guidance. ProtectUK. 2022. Available from: https://www.protectuk.police.uk/advice-and-guidance/risk/publicly-accessible-locations-pals-guidance ‌
  5. HOW TERRORIST GROUPS END: Lessons for Countering al Qa’ida. Limited Electronic Distribution Rights. Available from: https://www.rand.org/content/dam/rand/pubs/monographs/ 2008/RAND_MG741-1.pdf ‌
  6. Aolain FN, Campbell C. Managing Terrorism. J. Nat’l Sec. L. & Pol’y. 2017;9:367.
  7. Gayathri Y, Sri Lalitha Y, Aditya Nag MV, Althaf Hussain Basha S. Data-Driven Prediction Model for Crime Patterns. InSmart Computing Techniques and Applications: Proceedings of the Fourth International Conference on Smart Computing and Informatics, Volume 1 2021 (pp. 47-58). Springer Singapore.
  8. Goodchild MF, Janelle DG. Toward critical spatial thinking in the social sciences and humanities. GeoJournal. 2010 Feb;75:3-13.
  9. Kesavan T, Krishnamoorthy RK. An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction. Journal of Intelligent Systems. 2022 Aug 25;31(1):979-91.
  10. Valk CA, Lovei P, Cornelis H, Chuang Y, Visser T, Pu P, Lu Y. Identifying a Motivational Profile for Older Adults Towards Increased Physical Activity. International Journal of Design. 2021 Apr 1;15(1):17.
  11. Sharma A, Bajaj V, Arora J. Machine learning techniques for real-time emotion detection from facial expressions. In2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON) 2023 Feb 24 (pp. 1-6). IEEE.
Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 27/06/2024
Accepted 18/08/2024
Published 24/10/2024
195

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