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21 April 2020 Using AI/ML to predict perpetrators for terrorist incidents
Dinesh C. Verma, Scott Sigmund Gartner, Diane H. Felmlee, Dave Braines, Rithvik Yarlagadda
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Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114130G (2020) https://doi.org/10.1117/12.2558804
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
One of the key factors affecting any multi-domain operation concerns the influence of unorganized militias, which may often counter a more advanced adversary by means of terrorist incidents. In order to ensure the achievement of strategic objectives, the actions and influence of such violent activities need to be taken into account. However, in many cases, full information about the incidents that may have affected civilians and non-government organizations is hard to determine. In the situation of asymmetric warfare, or when planning a multi-domain operation, often the identity of the perpetrator may not themselves be known. In order to support a coalition commander's mandate, one could use AI/ML techniques to provide the missing details about incidents in the field which may only be partially understood or analyzed. In this paper, we examine the goal of predicting the identity of the perpetrator of a terrorist incident using AI/ML techniques on historical data, and discuss how well the AI/ML models can work to help clean the data available to the commander for data analysis.
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Dinesh C. Verma, Scott Sigmund Gartner, Diane H. Felmlee, Dave Braines, and Rithvik Yarlagadda "Using AI/ML to predict perpetrators for terrorist incidents", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114130G (21 April 2020); https://doi.org/10.1117/12.2558804
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KEYWORDS
Data modeling
Artificial intelligence
Data analysis
Machine learning
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