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Malicious URL classification using artificial fish swarm optimization and deep learning

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 syst...

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Main Authors: Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, K. Nour, Mohamed, M. Asiri, Mashael, M. Al-Sharafi, Ali, Othman, Mahmoud, Motwakel, Abdelwahed
Format: Article
Language:English
English
Published: Tech Science Press 2023
Subjects:
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/101886/7/101886_Malicious%20URL%20classification%20using%20artificial%20fish%20swarm.pdf
http://irep.iium.edu.my/101886/13/101886_Malicious%20URL%20classification%20using%20artificial%20fish%20swarm_SCOPUS.pdf
http://irep.iium.edu.my/101886/
http://doi.org/10.32604/cmc.2023.031371
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spelling my.iium.irep.1018862024-05-08T04:06:11Z http://irep.iium.edu.my/101886/ Malicious URL classification using artificial fish swarm optimization and deep learning Mustafa Hilal, Anwer Hassan Abdalla Hashim, Aisha G. Mohamed, Heba K. Nour, Mohamed M. Asiri, Mashael M. Al-Sharafi, Ali Othman, Mahmoud Motwakel, Abdelwahed TK7885 Computer engineering 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 Tech Science Press 2023 Article PeerReviewed application/pdf en http://irep.iium.edu.my/101886/7/101886_Malicious%20URL%20classification%20using%20artificial%20fish%20swarm.pdf application/pdf en http://irep.iium.edu.my/101886/13/101886_Malicious%20URL%20classification%20using%20artificial%20fish%20swarm_SCOPUS.pdf Mustafa Hilal, Anwer and Hassan Abdalla Hashim, Aisha and G. Mohamed, Heba and K. Nour, Mohamed and M. Asiri, Mashael and M. Al-Sharafi, Ali and Othman, Mahmoud and Motwakel, Abdelwahed (2023) Malicious URL classification using artificial fish swarm optimization and deep learning. Computers, Materials & Continua, 74 (1). pp. 607-621. ISSN 1546-2218 E-ISSN 1546-2226 http://doi.org/10.32604/cmc.2023.031371 10.32604/cmc.2023.031371
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Mustafa Hilal, Anwer
Hassan Abdalla Hashim, Aisha
G. Mohamed, Heba
K. Nour, Mohamed
M. Asiri, Mashael
M. Al-Sharafi, Ali
Othman, Mahmoud
Motwakel, Abdelwahed
Malicious URL classification using artificial fish swarm optimization and deep learning
description 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
format Article
author Mustafa Hilal, Anwer
Hassan Abdalla Hashim, Aisha
G. Mohamed, Heba
K. Nour, Mohamed
M. Asiri, Mashael
M. Al-Sharafi, Ali
Othman, Mahmoud
Motwakel, Abdelwahed
author_facet Mustafa Hilal, Anwer
Hassan Abdalla Hashim, Aisha
G. Mohamed, Heba
K. Nour, Mohamed
M. Asiri, Mashael
M. Al-Sharafi, Ali
Othman, Mahmoud
Motwakel, Abdelwahed
author_sort Mustafa Hilal, Anwer
title Malicious URL classification using artificial fish swarm optimization and deep learning
title_short Malicious URL classification using artificial fish swarm optimization and deep learning
title_full Malicious URL classification using artificial fish swarm optimization and deep learning
title_fullStr Malicious URL classification using artificial fish swarm optimization and deep learning
title_full_unstemmed Malicious URL classification using artificial fish swarm optimization and deep learning
title_sort malicious url classification using artificial fish swarm optimization and deep learning
publisher Tech Science Press
publishDate 2023
url http://irep.iium.edu.my/101886/7/101886_Malicious%20URL%20classification%20using%20artificial%20fish%20swarm.pdf
http://irep.iium.edu.my/101886/13/101886_Malicious%20URL%20classification%20using%20artificial%20fish%20swarm_SCOPUS.pdf
http://irep.iium.edu.my/101886/
http://doi.org/10.32604/cmc.2023.031371
_version_ 1800081752347115520
score 13.211869

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