IDEAS home Printed from https://ideas.repec.org/a/hin/complx/1373087.html
   My bibliography  Save this article

Prediction of Future Terrorist Activities Using Deep Neural Networks

Author

Listed:
  • M. Irfan Uddin
  • Nazir Zada
  • Furqan Aziz
  • Yousaf Saeed
  • Asim Zeb
  • Syed Atif Ali Shah
  • Mahmoud Ahmad Al-Khasawneh
  • Marwan Mahmoud

Abstract

One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

Suggested Citation

  • M. Irfan Uddin & Nazir Zada & Furqan Aziz & Yousaf Saeed & Asim Zeb & Syed Atif Ali Shah & Mahmoud Ahmad Al-Khasawneh & Marwan Mahmoud, 2020. "Prediction of Future Terrorist Activities Using Deep Neural Networks," Complexity, Hindawi, vol. 2020, pages 1-16, April.
  • Handle: RePEc:hin:complx:1373087
    DOI: 10.1155/2020/1373087
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2020/1373087.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2020/1373087.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2020/1373087?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Raj Bridgelall, 2022. "Applying unsupervised machine learning to counterterrorism," Journal of Computational Social Science, Springer, vol. 5(2), pages 1099-1128, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:1373087. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.