Machine Learning based Terrorist Attacks Prediction Algorithm

Research Article
Open access

Machine Learning based Terrorist Attacks Prediction Algorithm

Yuanyuan Wang 1
  • 1 Officers college of PAP    
  • *corresponding author
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220537
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

Terrorist attacks are spreading rapidly all over the world, which has caused heavy casualties and property losses. Therefore, it is very necessary to predict precisely the types of terrorist attacks and provide important information for counter-terrorism work. The existing research on terrorist attacks only analyzes a few characteristics, which leads to the limitations of the research. Therefore, this paper not only adds some continuous and discrete features, but also adds unstructured features based on the current research, which can accurately describe terrorist attacks. This paper proposes a random forest method based on threshold for feature selection because of the added characteristics of terrorist attacks, and then uses XGBoost model to predict the types of terrorist attacks. This method helps to prevent terrorist attacks and reduce the damage caused by terrorist attacks, and provide decision support for the counter-terrorism department to take measures in advance.

Keywords:

Random forest, Terrorist attacks, Feature selection

Wang,Y. (2023). Machine Learning based Terrorist Attacks Prediction Algorithm. Applied and Computational Engineering,2,694-700.
Export citation

References

[1]. Jiang L, Chen Y, Yu L et al. A Data Analysis Method for Anti-terrorism Based on Clustering. Information Research 6(260), 74-77(2019).

[2]. Yang Z, Li Y, Zhong Z. Research on Judgment of Suspects of Terrorist Attack Based on Data Mining. Information Research 4(258), 35-40(2019).

[3]. Liu M. Risk Assessment of Civil Aviation Terrorism Based on K-means Clustering. Data Analysis and Knowledge Discovery 10(22), 21-26(2018).

[4]. Xiao Y, Zhang Y. Terrorist attack organization prediction method based on feature selection and hyperparameter optimization. Journal of Computer Applications 40(8), 2262-2267(2020).

[5]. Li H, Zhang N, Cao Z et al. Terrorist Prediction Algorithm Based on Machine Learning. Computer Engineering 46(2), 315-320(2020).

[6]. Qiu L, Han X, Hu X. Study on method of consequence prediction for terrorist attacks based on machine learning. Journal of Safety Science and Technology 16(1), 175-181(2020).

[7]. Kumar, Vivek, et al. "A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks." International Conference in Software Engineering for Defence Applications. Springer, Cham, 2018.

[8]. Onyekachi, Uche Stanley, and Tsopze Norbert2& Ebem Deborah Uzoamaka. "Data Mining Approach to Counterterrorism."

[9]. Soliman, Ghada MA, and Tarek HM Abou-El-Enien. "Terrorism Prediction Using Artificial Neural Network." Rev. d'Intelligence Artif. 33.2 (2019): 81-87.

[10]. Wen X, Zhong A, Hu X. The Classification of Urban Greening Tree Species Based on Feature Selection of Random Forest. Journal of Geo-Information Science 12, 1777-1786(2018).

[11]. Zhao Z, Fu X, Jin X et al. Spam Message Recognition Based on Random Forest Feature Selection. Computer and Information Technology 6, 24-26(2018).

[12]. Cao Y, Zhu M, Wang X. Wind Turbine Blade Icing Forecast Based on Feature Selection and XBGoost. Electrical Automation 41(3), 31-33,118(2019).

[13]. Cao Rui, Liao Bin, Li M et al. Predicting Prices and Analyzing Features of Online Short-Term Rentals Based on XGBoost. Data Analysis and Knowledge Discovery 6: 51-65(2021).

[14]. Dong S. Data preprocessing technology in data mining. China Computer&Communication 16,144-145(2018).

[15]. Li Y, Mei J, Qin G. Research on Data Preprocessing in the Field of Counter Terrorism Intelligence Analysis. Information Science 35(11), 103-107,113(2017).

Cite this article

Wang,Y. (2023). Machine Learning based Terrorist Attacks Prediction Algorithm. Applied and Computational Engineering,2,694-700.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Jiang L, Chen Y, Yu L et al. A Data Analysis Method for Anti-terrorism Based on Clustering. Information Research 6(260), 74-77(2019).

[2]. Yang Z, Li Y, Zhong Z. Research on Judgment of Suspects of Terrorist Attack Based on Data Mining. Information Research 4(258), 35-40(2019).

[3]. Liu M. Risk Assessment of Civil Aviation Terrorism Based on K-means Clustering. Data Analysis and Knowledge Discovery 10(22), 21-26(2018).

[4]. Xiao Y, Zhang Y. Terrorist attack organization prediction method based on feature selection and hyperparameter optimization. Journal of Computer Applications 40(8), 2262-2267(2020).

[5]. Li H, Zhang N, Cao Z et al. Terrorist Prediction Algorithm Based on Machine Learning. Computer Engineering 46(2), 315-320(2020).

[6]. Qiu L, Han X, Hu X. Study on method of consequence prediction for terrorist attacks based on machine learning. Journal of Safety Science and Technology 16(1), 175-181(2020).

[7]. Kumar, Vivek, et al. "A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks." International Conference in Software Engineering for Defence Applications. Springer, Cham, 2018.

[8]. Onyekachi, Uche Stanley, and Tsopze Norbert2& Ebem Deborah Uzoamaka. "Data Mining Approach to Counterterrorism."

[9]. Soliman, Ghada MA, and Tarek HM Abou-El-Enien. "Terrorism Prediction Using Artificial Neural Network." Rev. d'Intelligence Artif. 33.2 (2019): 81-87.

[10]. Wen X, Zhong A, Hu X. The Classification of Urban Greening Tree Species Based on Feature Selection of Random Forest. Journal of Geo-Information Science 12, 1777-1786(2018).

[11]. Zhao Z, Fu X, Jin X et al. Spam Message Recognition Based on Random Forest Feature Selection. Computer and Information Technology 6, 24-26(2018).

[12]. Cao Y, Zhu M, Wang X. Wind Turbine Blade Icing Forecast Based on Feature Selection and XBGoost. Electrical Automation 41(3), 31-33,118(2019).

[13]. Cao Rui, Liao Bin, Li M et al. Predicting Prices and Analyzing Features of Online Short-Term Rentals Based on XGBoost. Data Analysis and Knowledge Discovery 6: 51-65(2021).

[14]. Dong S. Data preprocessing technology in data mining. China Computer&Communication 16,144-145(2018).

[15]. Li Y, Mei J, Qin G. Research on Data Preprocessing in the Field of Counter Terrorism Intelligence Analysis. Information Science 35(11), 103-107,113(2017).

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.