A Machine Learning Based Model of Boko Haram / by V. S. Subrahmanian, Chiara Pulice, James F. Brown, Jacob Bonen-Clark.

Format:

Book

Author/Creator:

Subrahmanian, V. S., Author.

Pulice, Chiara, Author.

Brown, James F., Author.

Bonen-Clark, Jacob., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Terrorism, security, and computation 2197-8786

Terrorism, Security, and Computation, 2197-8786

Language:

English

Subjects (All):

Machine learning.

Data mining.

Terrorism.

Political violence.

Machine Learning.

Data Mining and Knowledge Discovery.

Terrorism and Political Violence.

Local Subjects:

Machine Learning.

Data Mining and Knowledge Discovery.

Terrorism and Political Violence.

Physical Description:

1 online resource (XII, 135 pages) : 38 illustrations, 29 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

Chapter 1: Introduction

Chapter 2: History of Boko Haram

Chapter 3: Temporal Probabilistic Rules and Policy Computation Algorithms

Chapter 4: Sexual Violence

Chapter 5: Suicide Bombings

Chapter 6: Abductions

Chapter 7: Arson

Chapter 8: Other Types of Attacks

Appendix A: All TP-Rules

Appendix B: Data Collection

Appendix C: Most Used Variables

Appendix D: Sample Boko Haram Report.

Other Format:

Printed edition:

ISBN:

978-3-030-60614-5

9783030606145

Access Restriction:

Restricted for use by site license.