Using AI/ML to predict perpetrators for terrorist incidents

Dinesh C. Verma, Scott Sigmund Gartner, Diane Helen Felmlee, Dave Braines, Rithvik Yarlagadda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications II
EditorsTien Pham, Latasha Solomon, Katie Rainey
PublisherSPIE
ISBN (Electronic)9781510636033
DOIs
StatePublished - 2020
EventArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications II 2020 - Virtual, Online, United States
Duration: Apr 27 2020May 8 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11413
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications II 2020
CountryUnited States
CityVirtual, Online
Period4/27/205/8/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Using AI/ML to predict perpetrators for terrorist incidents'. Together they form a unique fingerprint.

  • Cite this

    Verma, D. C., Gartner, S. S., Felmlee, D. H., Braines, D., & Yarlagadda, R. (2020). Using AI/ML to predict perpetrators for terrorist incidents. In T. Pham, L. Solomon, & K. Rainey (Eds.), Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II [114130G] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11413). SPIE. https://doi.org/10.1117/12.2558804