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Submitted
20 September 2025
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19 November 2025
Published
31 December 2025
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Abstract

Phishing attacks are still the main and most severe vulnerability to cyber security through deceptive URLs for sensitive user information. Therefore, this paper proposes a framework based on deep learning techniques for phishing and legitimate URLs as an alternative to the currently employed blacklist-based detection approaches that have proven rather limited. The architecture includes one custom Convolutional Neural Network (CNN) and three Transfer Learning Architectures, ResNet50, InceptionV3, and VGG16 using representations of features based on URLs. All models under consideration shall be trained with Adam Optimizer and Binary Cross-Entropy loss function so that a fair comparison can be made under unified experimental set-up conditions. The set is broken down as 70% training, 10% validation, and 20% testing. Experimental results provide clear evidence that transfer learning models perform much better than the baseline CNN. The InceptionV3 model posted a validation accuracy of 100% leading the pack, followed by VGG16 at 98.96%. ResNet50 could muster only 97.92%. The proposed CNN model has also achieved quite competitive performance with a validation accuracy above 97%. Therefore, these results are explicit in confirming the effectiveness, robustness, and generalization capability of deep learning architectures toward the problem of phishing URL detection. This proposed framework will go a long way in ensuring web security is beefed up as it creates a barrier against all evolving cyber threats.

Keywords

Phishing URL detection Deep learning Cybersecurity Convolutional neural networks Transfer learning

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Copyright (c) 2025 Hind Abdulkareem Abdalrazaq (Author)

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Hind Abdulkareem Abdalrazaq, “A Robust Intelligent Approach for Phishing URL Detection Using Hybrid Machine Learning”, Cybersys. J, vol. 2, no. 2, pp. 91–97, Dec. 2025, doi: 10.57238/csj.2025.1017.
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[1]
Hind Abdulkareem Abdalrazaq, “A Robust Intelligent Approach for Phishing URL Detection Using Hybrid Machine Learning”, Cybersys. J, vol. 2, no. 2, pp. 91–97, Dec. 2025, doi: 10.57238/csj.2025.1017.
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