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<p class="xmsobodytext"><span style="font-size: 10.0pt;">One of the most influential actors in spreading Islamist violence across the Sahel is Jama’at Nasr Al Islam Wal Muslimin (JNIM).This book provides the first systematic quantitative analysis of JNIM’s behavior by analyzing a 12-year database of JNIM’s attacks and the environment surrounding JNIM. This book leverages AI/ML predictive models to accurately predict almost 40 types of attacks using over 80 independent variables.&#xa0;</span></p><p class="xmsobodytext"><span style="font-size: 10.0pt;">&#xa0;</span><span style="font-size: 10.0pt;">This book describes a set of temporal probabilistic rules that state that when the environment in which the group operates satisfies some conditions, then an attack of a certain type will likely occur in the next N months.&#xa0; This provides a deep, easy to comprehend understanding of the conditions under which JNIM carries various kinds of attacks up to 6 months into the future. </span></p><p class="xmsobodytext"><span style="font-size: 10.0pt;">&#xa0;</span><span style="font-size: 10.0pt;">This book will serve as an invaluable guide to scholars (computer scientists, political scientists, policy makers). Military officers, intelligence personnel, and government employees, who seek to understand, predict, and eventually mitigate attacks by JNIM and bring peace to the nations of Mali, Burkina Faso, and Niger will want to purchase this book as well.</span></p>

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Machine Learning Techniques to Predict Terrorist Attacks

  • 书籍

  • 发表日期：

    2025年

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  • Laura Mostert,
  • Roy Lindelauf,
  • Chiara Pulice,
  • Marnix Provoost,
  • Priyanka Amin,
  • V.S. Subrahmanian

摘要

One of the most influential actors in spreading Islamist violence across the Sahel is Jama’at Nasr Al Islam Wal Muslimin (JNIM).This book provides the first systematic quantitative analysis of JNIM’s behavior by analyzing a 12-year database of JNIM’s attacks and the environment surrounding JNIM. This book leverages AI/ML predictive models to accurately predict almost 40 types of attacks using over 80 independent variables. 

 This book describes a set of temporal probabilistic rules that state that when the environment in which the group operates satisfies some conditions, then an attack of a certain type will likely occur in the next N months.  This provides a deep, easy to comprehend understanding of the conditions under which JNIM carries various kinds of attacks up to 6 months into the future.

 This book will serve as an invaluable guide to scholars (computer scientists, political scientists, policy makers). Military officers, intelligence personnel, and government employees, who seek to understand, predict, and eventually mitigate attacks by JNIM and bring peace to the nations of Mali, Burkina Faso, and Niger will want to purchase this book as well.