Citation

Mushtaq, Shougfta and Mohd Su’ud, Mazliham (2026) Detection of Malicious URLs: A Deep Learning and Machine Learning Perspective. International Journal on Robotics Automation and Sciences, 8 (1). p. 1. ISSN 2682-860X

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Abstract

Nowadays, as the cyber world is expanding rapidly, issues related to cybersecurity are also increasing. The criminal mind set try to breach the security of individual or organization by firstly, win confidence and secondly attack them for this purpose URL phishing is a most common way where phisher attach a link and share with victim. The proposed paper examines the various machine learning and deep learning approaches on state-of-the-art data set Crawling2024 by classifying the phishing and legitimate URLs. The study involves different machine learning algorithms like Random Forest, LR (Logistic Regression), XGBoost, MLP (Multilayer Perceptron) and Gated Recurrent Units (GRU) and deep learning algorithms like CNN (Convolutional Neural Network), MLP, etc. and analyze performance metrics accuracy, precision, F1 score, Recall, False Positive and False Negative. The RF (Random Forest) classifier achieved the highest precision (98.63%) and accuracy (96.24%), while Logistic Regression and GRU also achieved well. In addition to that LTRCN (Long-Term Recurrent Convolutional Network) achieved good precision but poor accuracy 48.23%. The experimental work shows that conventional algorithms such as Random Forest and advanced algorithms like GRU are efficient in detecting URL phishing, it also emphasizes that there is still need of some advanced approaches like CNN and LTRCN.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, deep learning, cybersecurity.
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	09 Jul 2026 04:07
Last Modified:	09 Jul 2026 04:07
URII:	http://shdl.mmu.edu.my/id/eprint/16352

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