Abstract
There has been a sudden rapid increase in the number of malicious webpages in the recent years, so the process of determining the authenticity of a web link has become very difficult. Malicious webpage developers have been able to evade the signature-based detection techniques very easily and comfortably. The malicious webpage can be detected through basically static and dynamic analysis. In this paper, dynamic deep learning techniques have been explored that convert the links of the webpages to an image file and classifies the web pages as malicious or benign. The deep learning techniques (VGG 16 and VGG 19) are utilized for the classification of web pages. These models provide an accuracy of 99.8% in classifying malware webpage image file samples.
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Krishna, K., Choudhary, J., Singh, D.P. (2022). Malicious Webpage Classification Using Deep Learning Technique. In: Dubey, H.M., Pandit, M., Srivastava, L., Panigrahi, B.K. (eds) Artificial Intelligence and Sustainable Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-1220-6_39
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DOI: https://doi.org/10.1007/978-981-16-1220-6_39
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