A Machine Learning Based Model of Boko Haram (Terrorism, Security, and Computation)

by V.S. Subrahmanian, Chiara Pulice, James F Brown, and Jacob Bonen-Clark

Geert Kuiper (Foreword)

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Book cover for A Machine Learning Based Model of Boko Haram

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This is the first study of Boko Haram that brings advanced data-driven, machine learning models to both learn models capable of predicting a wide range of attacks carried out by Boko Haram, as well as develop data-driven policies to shape Boko Haram's behavior and reduce attacks by them. This book also identifies conditions that predict sexual violence, suicide bombings and attempted bombings, abduction, arson, looting, and targeting of government officials and security installations.

After reducing Boko Haram's history to a spreadsheet containing monthly information about different types of attacks and different circumstances prevailing over a 9 year period, this book introduces Temporal Probabilistic (TP) rules that can be automatically learned from data and are easy to explain to policy makers and security experts. This book additionally reports on over 1 year of forecasts made using the model in order to validate predictive accuracy. It also introduces a policy computation method to rein in Boko Haram's attacks.

Applied machine learning researchers, machine learning experts and predictive modeling experts agree that this book is a valuable learning asset. Counter-terrorism experts, national and international security experts, public policy experts and Africa experts will also agree this book is a valuable learning tool.

  • ISBN10 3030606139
  • ISBN13 9783030606138
  • Publish Date 12 December 2020
  • Publish Status Active
  • Publish Country CH
  • Imprint Springer Nature Switzerland AG
  • Edition 1st ed. 2021
  • Format Hardcover
  • Pages 135
  • Language English