Abstract
We approach the phishing detection problem as a data science problem with three different sub-models, each designed to handle a specific sub-task: URL classification, webpage classification based on HTML content and logo detection and recognition from the screenshot of a given webpage. The combined results from the sub-models are used as input for an ensemble model designed to do the classification. Based on the analysis performed on the results, one may conclude that ML techniques are suitable to be part of a system designed for automatic phishing detection.
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References
Prakash, P., Kumar, M., Kompella, R., Gupta, M.: PhishNet: predictive blacklisting to detect phishing attacks. In: 2010 Proceedings IEEE INFOCOM, pp. 1–5 (2010)
Mohammad, R.M., Thabtah, F.A., McCluskey, T.: Intelligent rule-based phishing websites classification. IET Inf. Secur. 8, 153–160 (2014)
Le, H., Pham, Q., Sahoo, D., Hoi, S. URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection. arXiv, abs/1802.03162 (2018)
Opara, C., Wei, B., Chen, Y.: HTMLPhish: enabling phishing web page detection by applying deep learning techniques on HTML analysis. In: 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1–8 (2020). https://doi.org/10.1109/IJCNN48605.2020.9207707
Li, Y., Yang, Z., Chen, X., Yuan, H., Liu, W.: A stacking model using URL and HTML features for phishing webpage detection. Future Gener. Comput. Syst. 94, 27–39 (2019)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, p. 13. arxiv:1810.04805Comment (2018)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: Fully Convolutional One-Stage Object Detection. CoRR, abs/1904.01355 (2019)
Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., Brown, D.: Text classification algorithms: a survey. Information 10(4), 150 (2019)
Liu, W., et al.: SSD: Single Shot MultiBox Detector. arXiv, abs/1512.02325 (2016)
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal Loss for Dense Object Detection. arXiv:1708.02002 (2017)
Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv, abs/1804.02767 (2018)
Zhang, X., Zhao, J., Lecun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, Jan 2015, pp. 649–657 (2015)
Geng, G., Lee, X., Zhang, Y.: Combating phishing attacks via brand identity and authorization features. Security Commun. Netw. 8, 888–898 (2015). https://doi.org/10.1002/sec.1045
Darknet: Open Source Neural Networks in C. http://pjreddie.com/darknet/. Accessed 12 Oct 2020
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 Oct 2014, Doha, Qatar. A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1746–1751 (2014)
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Vecliuc, DD., Artene, CG., Tibeică, MN., Leon, F. (2021). An Experimental Study of Machine Learning for Phishing Detection. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_34
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DOI: https://doi.org/10.1007/978-3-030-73280-6_34
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