Predicting the success of suicide terrorist attacks using different machine learning algorithms

Hossain, Md Junayed and Abdullah, Sheikh Md and Barkatullah, Mohammad and Miahh, Md Saef Ulla and Sarwar, Talha and Monir, Md Fahad (2022) Predicting the success of suicide terrorist attacks using different machine learning algorithms. In: Proceedings of 2022 25th International Conference on Computer and Information Technology, ICCIT 2022 , 17-19 December 2022 , Cox's Bazar. pp. 1-6. (187046). ISBN 979-835034602-2

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Abstract

Extremism has become one of the major threats throughout the world over the past few decades. In the last two decades, there has been a sharp increase in extremism and terrorist attacks. Nowadays, terrorism concerns all nations in terms of national security and is considered one of the most priority research topics. In order to support the national defense system, academics and researchers are analyzing various datasets to determine the reasons behind these attacks, their patterns, and how to predict their success. The main objective of our paper is to predict different types of attacks, such as successful suicide attacks, successful non-suicide attacks, unsuccessful suicide attacks, and unsuccessful non-suicide attacks. For this purpose, various machine learning algorithms, namely Random Forest, K Nearest Neighbor, Decision Tree, LightGBM Boosting, and a feedforward Artificial Neural Network called Multilayer Perceptron (MLP), are used to determine the success of suicide terrorist attacks. With an accuracy rate of 98.4% and an AUC-ROC score of 99.9%, the Random Forest classifier was the most accurate among all other algorithms. This model is more trustworthy than previous work and provides a useful comparison between machine learning methods and an artificial neural network because it is less dependent and has a multiclass target feature.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	GTD; Machine learning; Suicide terrorist attack; Terrorism
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Institute of Postgraduate Studies College of Engineering Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	14 Nov 2023 03:46
Last Modified:	14 Nov 2023 03:46
URI:	http://umpir.ump.edu.my/id/eprint/39083
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