Abstract
Malicious content on the web became a global risk at web users, its huge spreading made users vulnerable to all types of cyber attacks that can be performed behind websites. Many recent researches were proposed to detect malicious content. In this work, we propose a chrome extension to defeat the majority types of malicious web content by analyzing URL and page content. The extension is based on a relevant list of malicious features geared with a machine learning classifier also it integrated Google safe browsing for more precision. The obtained experimental results demonstrate the effectiveness of our extension for detecting the malicious content which proved by the perfect result scored on several classifiers with 99.5% accuracy.
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Haoud, M., Djehiche, R., Saoudi, L. (2021). Web Guard. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1365. Springer, Cham. https://doi.org/10.1007/978-3-030-72657-7_34
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DOI: https://doi.org/10.1007/978-3-030-72657-7_34
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-030-72657-7
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