Machine Learning Techniques to Predict Terrorist Attacks

One of the most influential actors in spreading such 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. The book leverages AI/ML predictive models to accurately predict almost 40 types of attacks using over 80 independent variables. The 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 upto 6 months into the future. The 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.