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1 June 2023 Multimodal malicious web page detection technology based on content and image fusion
Dengcong Zhu, Hanbing Yao, Ming Zhang
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Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127181E (2023) https://doi.org/10.1117/12.2681701
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China
Abstract
Most of the existing malicious webpage detection technologies are single-mode, without considering the internal relationship between the content of the webpage and the image. In view of the above problems, this paper proposes a malicious webpage detection technology based on Transformer-MDA-ESIM multi-modal fusion. Use the BERT model and the VGG16 model to extract the feature vectors of the content of the web page and the image. The two extracted feature vectors are input to the Transformer layer for encoding, which solves the problem of difficulty in capturing long-distance dependencies. The text semantics is enhanced by mining the connection between the content of the web page and the image through the dual attention mechanism of the semantic enhancement layer. The ESIM interactive attention layer is used to obtain the similar features of text content and images, realize the fusion of text features and image features and improve the model's ability to understand web pages. The experimental results show that this method has better performance in malicious webpage identification compared with single modality malicious webpage detection technology.
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Dengcong Zhu, Hanbing Yao, and Ming Zhang "Multimodal malicious web page detection technology based on content and image fusion", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127181E (1 June 2023); https://doi.org/10.1117/12.2681701
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KEYWORDS
Feature extraction
Transformers
Internet
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