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

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 This is the first study on Boko Haram to incorporate advanced data-driven machine learning models, both to learn models that can predict a wide range of attacks carried out by Boko Haram, and to develop data-driven policies to shape Boko Haram’s behavior and reduce its attacks. This book also identifies conditions that predict sexual violence, suicide bombings and attempted bombings, kidnapping, arson, looting, and attacks on government officials and security facilities.

 After reducing the history of Boko Haram to a spreadsheet containing monthly information on different types of attacks and different circumstances prevailing over a 9-year period, this book presents temporal probability (TP) rules that can be automatically learned from data and are easy to explain to policymakers and security experts. This book also reports on over a year of predictions made using the model to verify the accuracy of the predictions. It also presents a method for calculating policies to curb Boko Haram attacks.
 Applied machine learning researchers, machine learning experts, and predictive modeling experts agree that this book is a valuable learning asset. Counterterrorism experts, national and international security experts, public policy experts, and Africa experts will also agree that this book is a valuable learning tool. To purchase the book, click here