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ThreatSense: Terrorist attack Prediction Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Machine Learning
DOI: https://doi.org/10.64388/IREV9I10-1716519
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.
How to cite this paper
@article{1716519,
author = {Tanishq Kumar Singh, Yaseen Khan, Vaibhav Rana},
title = {ThreatSense: Terrorist attack Prediction Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {4007-4014},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716519.pdf},
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.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716519}
}