Malicious web content detection using machine leaning

Anand Desai, J. Jatakia, Rohit Naik, Nataasha Raul
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引用次数: 26

Abstract

Naive users using a browser have no idea about the back-end of the page. The users might be tricked into giving away their credentials or downloading malicious data. Our aim is to create an extension for Chrome which will act as middleware between the users and the malicious websites, and mitigate the risk of users succumbing to such websites. Further, all harmful content cannot be exhaustively collected as even that is bound to continuous development. To counter this we are using machine learning-to train the tool and categorize the new content it sees every time into the particular categories so that corresponding action can be taken.
恶意网页内容检测使用机器学习
使用浏览器的新手用户不知道页面的后端是什么。用户可能会被欺骗,泄露他们的凭据或下载恶意数据。我们的目标是为Chrome创建一个扩展,它将作为用户和恶意网站之间的中间件,并降低用户屈服于此类网站的风险。此外,所有有害的内容不能被彻底收集,因为即使这样也必然会持续发展。为了解决这个问题,我们正在使用机器学习来训练工具,并将每次看到的新内容分类到特定的类别中,以便采取相应的行动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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