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A Malicious Webpage Detection Method Based on Graph Convolutional Network

Author

Listed:
  • Yilin Wang

    (School of Computer Science, China University of Geosciences, Wuhan 430074, China)

  • Siqing Xue

    (School of Computer Science, China University of Geosciences, Wuhan 430074, China)

  • Jun Song

    (School of Computer Science, China University of Geosciences, Wuhan 430074, China)

Abstract

In recent years, with the rapid development of the Internet and information technology, video websites, shopping websites, and other portals have grown rapidly. However, malicious webpages can disguise themselves as benign websites and steal users’ private information, which seriously threatens network security. Current detection methods for malicious webpages do not fully utilize the syntactic and semantic information in the web source code. In this paper, we propose a GCN-based malicious webpage detection method (GMWD), which constructs a text graph to describe and then a GCN model to learn the syntactic and semantic correlations within and between webpage source codes. We replace word nodes in the text graph with phrase nodes to better maintain the syntactic and semantic integrity of the webpage source code. In addition, we use the URL links appearing in the source code as auxiliary detection information to further improve the detection accuracy. The experiments showed that the proposed method can achieve 99.86% accuracy and a 0.137% false negative rate, achieving a better performance than other related malicious webpage detection methods.

Suggested Citation

  • Yilin Wang & Siqing Xue & Jun Song, 2022. "A Malicious Webpage Detection Method Based on Graph Convolutional Network," Mathematics, MDPI, vol. 10(19), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3496-:d:924637
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    References listed on IDEAS

    as
    1. Ankit Kumar Jain & B. B. Gupta, 2018. "Towards detection of phishing websites on client-side using machine learning based approach," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(4), pages 687-700, August.
    2. You Guo & Hector Marco-Gisbert & Paul Keir, 2020. "Mitigating Webshell Attacks through Machine Learning Techniques," Future Internet, MDPI, vol. 12(1), pages 1-16, January.
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