Using AI/ML to predict perpetrators for terrorist incidents

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 incident 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.
  • Dinesh Verma (IBM US)
  • Scott Sigmund Gartner (PSU)
  • Diane Felmlee (PSU)
  • Dave Braines (IBM UK)
  • Rithvik Yarlagadda (PSU)
Date May-2020
Venue SPIE DCS 2020