Swamy, Abhishek and Kumar, Dinesh (2024) Phishing Detection Using a Convolutional Neural Network Model on Website URLs. [Conference Proceedings]
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According to recent statistics, most business activity is now conducted online via the internet and through websites. This shift to online commerce has led to risk of exposure to cyber threats, such as phishing. Phishing detection remains a challenge, with traditional blacklisting methods struggling to keep pace with the exponential growth of malicious websites. As a result, phishing attacks have become more frequent and sophisticated and therefore requires newer and smarter ways of detection. This paper proposes a two-step, computer vision-based technique for phishing detection. In the first step, URLs are converted into images. Then, a Convolutional Neural Network (CNN) is employed to analyse these images and classify them as phishing or legitimate. Our experiments demonstrate promising results, indicating that the proposed method outperforms traditional approaches in phishing URL detection, such as blacklisting. This finding highlights the potential of utilizing URL-to-image conversion and CNNs for effective phishing website classification.
| Item Type: | Conference Proceedings |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Dinesh Kumar |
| Date Deposited: | 20 Jul 2025 21:59 |
| Last Modified: | 20 Jul 2025 21:59 |
| URI: | https://repository.usp.ac.fj/id/eprint/15035 |
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