Malicious web content detection by machine learning

doi:10.1016/j.eswa.2009.05.023

Malicious web content detection by machine learning

2010-01-01

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DOI：10.1016/j.eswa.2009.05.023

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The recent development of the dynamic HTML gives attackers a new and powerful technique to compromise computer systems. A Malicious dynamic HTML code is usually embedded in a normal webpage. The malicious webpage infects the victim when a user browses it. Furthermore such DHTML code can disguise itself easily through obfuscation or transformation, which makes the detection even harder. Anti-virus software packages commonly use signature-based approaches which might not be able to efficiently identify camouflaged malicious HTML codes. Therefore, our paper proposes a malicious web page detection using the technique of machine learning. Our study analyzes the characteristic of a malicious webpage systematically and presents important features for machine learning. Experimental results demonstrate that our method is resilient to code obfuscations and can correctly determine whether a webpage is malicious or not. (C) 2009 Elsevier Ltd. All rights reserved.

Keywords:

Dynamic HTML

Malicious webpage

Machine learning

Malicious web content detection by machine learning

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