Predicting the success of suicide terrorist attacks using different machine learning algorithms
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...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39083/ http://umpir.ump.edu.my/id/eprint/39083/1/Predicting%20the%20success%20of%20suicide%20terrorist%20attacks%20using%20different%20machine.pdf http://umpir.ump.edu.my/id/eprint/39083/2/Predicting%20the%20success%20of%20suicide%20terrorist%20attacks%20using%20different%20machine%20learning%20algorithms_ABS.pdf |
| _version_ | 1848825678775975936 |
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| author | Hossain, Md Junayed Abdullah, Sheikh Md Barkatullah, Mohammad Miahh, Md Saef Ulla Sarwar, Talha Monir, Md Fahad |
| author_facet | Hossain, Md Junayed Abdullah, Sheikh Md Barkatullah, Mohammad Miahh, Md Saef Ulla Sarwar, Talha Monir, Md Fahad |
| author_sort | Hossain, Md Junayed |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:32:45Z |
| format | Conference or Workshop Item |
| id | ump-39083 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:32:45Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-390832023-11-14T03:46:05Z http://umpir.ump.edu.my/id/eprint/39083/ Predicting the success of suicide terrorist attacks using different machine learning algorithms Hossain, Md Junayed Abdullah, Sheikh Md Barkatullah, Mohammad Miahh, Md Saef Ulla Sarwar, Talha Monir, Md Fahad QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) 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. Institute of Electrical and Electronics Engineers Inc. 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39083/1/Predicting%20the%20success%20of%20suicide%20terrorist%20attacks%20using%20different%20machine.pdf pdf en http://umpir.ump.edu.my/id/eprint/39083/2/Predicting%20the%20success%20of%20suicide%20terrorist%20attacks%20using%20different%20machine%20learning%20algorithms_ABS.pdf 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 (Published) https://doi.org/10.1109/ICCIT57492.2022.10055100 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Hossain, Md Junayed Abdullah, Sheikh Md Barkatullah, Mohammad Miahh, Md Saef Ulla Sarwar, Talha Monir, Md Fahad Predicting the success of suicide terrorist attacks using different machine learning algorithms |
| title | Predicting the success of suicide terrorist attacks using different machine learning algorithms |
| title_full | Predicting the success of suicide terrorist attacks using different machine learning algorithms |
| title_fullStr | Predicting the success of suicide terrorist attacks using different machine learning algorithms |
| title_full_unstemmed | Predicting the success of suicide terrorist attacks using different machine learning algorithms |
| title_short | Predicting the success of suicide terrorist attacks using different machine learning algorithms |
| title_sort | predicting the success of suicide terrorist attacks using different machine learning algorithms |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/39083/ http://umpir.ump.edu.my/id/eprint/39083/ http://umpir.ump.edu.my/id/eprint/39083/1/Predicting%20the%20success%20of%20suicide%20terrorist%20attacks%20using%20different%20machine.pdf http://umpir.ump.edu.my/id/eprint/39083/2/Predicting%20the%20success%20of%20suicide%20terrorist%20attacks%20using%20different%20machine%20learning%20algorithms_ABS.pdf |