ThreatSense: Terrorist attack Prediction Using Machine Learning

• Author(s): Tanishq Kumar Singh; Yaseen Khan; Vaibhav Rana
• Paper ID: 1716519
• Page: 4007-4014
• Published Date: 05-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 10 April-2026

Abstract

Terrorism remains one of the major global security threats affecting public safety and national stability. Early detection of potential terrorist activities can significantly reduce damage and loss of life. In this research, we present ThreatSense, a terrorist attack prediction system based on machine learning and IoT sensor networks. The system integrates data from surveillance cameras, motion sensors, acoustic sensors, and social media feeds to detect suspicious activities. Advanced algorithms such as XGBoost and LSTM are used to analyze multi-modal data and predict threat levels. To improve transparency and reliability, the system also incorporates explainable AI techniques like SHAP to identify which features contribute to threat prediction. The model classifies threat levels into five categories ranging from low risk to critical threat. Overall, this paper explains system design, methodology, data analysis, results, and future scope showing how AI and IoT can support proactive threat detection.

Keywords

Terrorism Prediction, Machine Learning, IoT Sensors, XGBoost, LSTM, Explainable AI, SHAP, Multiclass Classification, Threat Detection.

Citations

IRE Journals:
Tanishq Kumar Singh, Yaseen Khan, Vaibhav Rana "ThreatSense: Terrorist attack Prediction Using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 4007-4014 https://doi.org/10.64388/IREV9I10-1716519

IEEE:
Tanishq Kumar Singh, Yaseen Khan, Vaibhav Rana "ThreatSense: Terrorist attack Prediction Using Machine Learning" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716519