TY - GEN
T1 - Using AI/ML to predict perpetrators for terrorist incidents
AU - Verma, Dinesh C.
AU - Gartner, Scott Sigmund
AU - Felmlee, Diane H.
AU - Braines, Dave
AU - Yarlagadda, Rithvik
N1 - Funding Information:
This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001 and the Penn State Center for Security Research and Education. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
Publisher Copyright:
© 2020 SPIE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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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U2 - 10.1117/12.2558804
DO - 10.1117/12.2558804
M3 - Conference contribution
AN - SCOPUS:85089186441
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II
A2 - Pham, Tien
A2 - Solomon, Latasha
A2 - Rainey, Katie
PB - SPIE
T2 - Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II 2020
Y2 - 27 April 2020 through 8 May 2020
ER -