Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.eswa.2009.05.023
DC Field | Value | |
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dc.title | Malicious web content detection by machine learning | |
dc.contributor.author | Hou Y.-T. | |
dc.contributor.author | Chang Y. | |
dc.contributor.author | Chen T. | |
dc.contributor.author | Laih C.-S. | |
dc.contributor.author | Chen C.-M. | |
dc.date.accessioned | 2018-08-21T05:01:41Z | |
dc.date.available | 2018-08-21T05:01:41Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Hou Y.-T., Chang Y., Chen T., Laih C.-S., Chen C.-M. (2010). Malicious web content detection by machine learning. Expert Systems with Applications 37 (1) : 55-60. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2009.05.023 | |
dc.identifier.issn | 09574174 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/146186 | |
dc.description.abstract | 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. | |
dc.source | Scopus | |
dc.subject | Dynamic HTML | |
dc.subject | Machine learning | |
dc.subject | Malicious webpage | |
dc.type | Article | |
dc.contributor.department | OFFICE OF THE PROVOST | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.1016/j.eswa.2009.05.023 | |
dc.description.sourcetitle | Expert Systems with Applications | |
dc.description.volume | 37 | |
dc.description.issue | 1 | |
dc.description.page | 55-60 | |
dc.description.coden | ESAPE | |
dc.published.state | published | |
Appears in Collections: | Staff Publications |
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