Image-Based Phishing URL Classification Using Convolutional Neural Networks
محورهای موضوعی : IT Strategy
Gholamreza Ahmadi
1
,
Hamed Monkaresi
2
1 -
2 - دانشگاه رازی
کلید واژه: Phishing Detection, URL Classification, Convolutional Neural Networks (CNNs), Deep Learning, Image-Based Classification, Cybersecurity,
چکیده مقاله :
Phishing attacks continue to pose a significant threat to online security, with attackers increasingly leveraging deceptive URLs to steal sensitive information. Traditional phishing detection methods often rely on URL analysis or manual feature extraction, which can be time-consuming and less effective against evolving attack techniques. To address these limitations, more adaptive and intelligent detection mechanisms are increasingly required to keep pace with modern attack strategies. In this paper, we propose an image-based approach for phishing URL classification using Convolutional Neural Networks (CNNs). By transforming URLs into visual representations based on their features, we leverage the power of deep learning to automatically extract discriminative features for classification. We conduct a comprehensive comparison of various deep learning models, including different CNN architectures (both basic and pre-trained/fine-tuned), to evaluate their performance in terms of accuracy, computational efficiency, and training time. Our experiments demonstrate that image-based classification using CNNs achieves competitive accuracy while offering potential robustness against adversarial variations in phishing URLs. Additionally, we analyze the trade-offs between model complexity and inference time, providing insights into the practical deployment of such systems. The results highlight the potential of image-based deep learning models as an effective tool for phishing detection, paving the way for further research in this domain.
Phishing attacks continue to pose a significant threat to online security, with attackers increasingly leveraging deceptive URLs to steal sensitive information. Traditional phishing detection methods often rely on URL analysis or manual feature extraction, which can be time-consuming and less effective against evolving attack techniques. To address these limitations, more adaptive and intelligent detection mechanisms are increasingly required to keep pace with modern attack strategies. In this paper, we propose an image-based approach for phishing URL classification using Convolutional Neural Networks (CNNs). By transforming URLs into visual representations based on their features, we leverage the power of deep learning to automatically extract discriminative features for classification. We conduct a comprehensive comparison of various deep learning models, including different CNN architectures (both basic and pre-trained/fine-tuned), to evaluate their performance in terms of accuracy, computational efficiency, and training time. Our experiments demonstrate that image-based classification using CNNs achieves competitive accuracy while offering potential robustness against adversarial variations in phishing URLs. Additionally, we analyze the trade-offs between model complexity and inference time, providing insights into the practical deployment of such systems. The results highlight the potential of image-based deep learning models as an effective tool for phishing detection, paving the way for further research in this domain.
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