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http://dx.doi.org/10.9708/jksci.2012.17.3.077

Classification of Malicious Web Pages by Using SVM  

Hwang, Young-Sup (Dept. of Computer Science and Engineering, Sun Moon University)
Moon, Jae-Chan (Dept. of Computer Science and Software Science, Dankook University)
Cho, Seong-Je (Dept. of Computer Science and Software Science, Dankook University)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.3, 2012 , pp. 77-83 More about this Journal
Abstract
As web pages provide various services, the distribution of malware via the web pages is being also increased. Malware can make personal information leak, system mal-function and system be zombie. To protect this damages, we should block the malicious web pages. Because the malicious codes embedded in web pages are obfuscated or transformed, it is difficult to detect them using signature-based approaches which are used by current anti-virus software. To overcome this problem, we extracted features to classify malicious web pages and benign ones by analyzing web pages. And we propose a classification method using SVM which is widely used in machine learning. Experimental results show that the proposed method is better than other methods. The proposed method could classify malicious web pages correctly and be helpful to block the distribution of malicious codes.
Keywords
Malicious web page; SVM; malware; obfuscation; machine learning;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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