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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Anwer Mustafa Hilal1,2,*, Aisha Hassan Abdalla Hashim1, Heba G. Mohamed3, Mohamed K. Nour4, Mashael M. Asiri5, Ali M. Al-Sharafi6, Mahmoud Othman7, Abdelwahed Motwakel2

1 Department of Electrical and Computer Engineering, International Islamic University Malaysia 53100 Kuala Lumpur, Malaysia
2 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
3 Department of Electrical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
5 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
6 Department of Computer Science, College of Computers and Information Technology, University of Bisha, Saudi Arabia
7 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt

* Corresponding Author: Anwer Mustafa Hilal. Email: email

Computers, Materials & Continua 2023, 74(1), 607-621. https://doi.org/10.32604/cmc.2023.031371

Abstract

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs. To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model. The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository. The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

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Cite This Article

APA Style
Hilal, A.M., Hashim, A.H.A., Mohamed, H.G., Nour, M.K., Asiri, M.M. et al. (2023). Malicious URL classification using artificial fish swarm optimization and deep learning. Computers, Materials & Continua, 74(1), 607-621. https://doi.org/10.32604/cmc.2023.031371
Vancouver Style
Hilal AM, Hashim AHA, Mohamed HG, Nour MK, Asiri MM, Al-Sharafi AM, et al. Malicious URL classification using artificial fish swarm optimization and deep learning. Comput Mater Contin. 2023;74(1):607-621 https://doi.org/10.32604/cmc.2023.031371
IEEE Style
A.M. Hilal et al., "Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning," Comput. Mater. Contin., vol. 74, no. 1, pp. 607-621. 2023. https://doi.org/10.32604/cmc.2023.031371



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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