IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i24p16811-d1299546.html
   My bibliography  Save this article

Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model

Author

Listed:
  • Mohammed Aljebreen

    (Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia)

  • Fatma S. Alrayes

    (Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Sumayh S. Aljameel

    (Saudi Aramco Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)

  • Muhammad Kashif Saeed

    (Department of Computer Science, Applied College, King Khalid University, P.O. Box 9004, Abha 62529, Saudi Arabia)

Abstract

With the enhancement of the Internet of Things (IoT), smart cities have developed the idea of conventional urbanization. IoT networks permit distributed smart devices to collect and process data in smart city structures utilizing an open channel, the Internet. Accordingly, challenges like security, centralization, privacy (i.e., execution data poisoning and inference attacks), scalability, transparency, and verifiability restrict faster variations of smart cities. Detecting malicious URLs in an IoT environment is crucial to protect devices and the network from potential security threats. Malicious URL detection is an essential element of cybersecurity. It is established that malicious URL attacks mean large risks in smart cities, comprising financial damages, losses of personal identifications, online banking, losing data, and loss of user confidentiality in online businesses, namely e-commerce and employment of social media. Therefore, this paper concentrates on the proposal of a Political Optimization Algorithm by a Hybrid Deep Learning Assisted Malicious URL Detection and Classification for Cybersecurity (POAHDL-MDC) technique. The presented POAHDL-MDC technique identifies whether malicious URLs occur. To accomplish this, the POAHDL-MDC technique performs pre-processing to transform the data to a compatible format, and a Fast Text word embedding process is involved. For malicious URL recognition, a Hybrid Deep Learning (HDL) model integrates the features of stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM). Finally, POA is exploited for optimum hyperparameter tuning of the HDL technique. The simulation values of the POAHDL-MDC approach are tested on a Malicious URL database, and the outcome exhibits an improvement of the POAHDL-MDC technique with a maximal accuracy of 99.31%.

Suggested Citation

  • Mohammed Aljebreen & Fatma S. Alrayes & Sumayh S. Aljameel & Muhammad Kashif Saeed, 2023. "Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16811-:d:1299546
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/24/16811/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/24/16811/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Chrisbel Simisterra-Batallas & Pablo Pico-Valencia & Jaime Sayago-Heredia & Xavier Quiñónez-Ku, 2025. "Internet of Things and Deep Learning for Citizen Security: A Systematic Literature Review on Violence and Crime," Future Internet, MDPI, vol. 17(4), pages 1-30, April.

    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:gam:jsusta:v:15:y:2023:i:24:p:16811-:d:1299546. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.