Grégoire Geis

PhishGNN: A phishing website detection framework using graph neural networks

By Tristan Bilot, Grégoire Geis, Badis Hammi

2022-07-01

In Proceedings of the 19th international conference on security and cryptography - SECRYPT

Abstract

Because of the importance of the web in our daily lives, phishing attacks have been causing a significant damage to both individuals and organizations. Indeed, phishing attacks are today among the most widespread and serious threats to the web and its users. The main approaches deployed against such attacks are blacklists. However, the latter represents numerous drawbacks. In this paper, we introduce PhishGNN, a Deep Learning framework based on Graph Neural Networks, which leverages and uses the hyperlink graph structure of web- sites along with different other hand-designed features. The performance results obtained, demonstrate that PhishGNN outperforms state of the art results with a 99.7% prediction accuracy.

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