Classifying Malicious Web Pages by Using an Adaptive Support Vector Machine

Young Sup Hwang, Jin Baek Kwon, Jae Chan Moon and Seong Je Cho
Volume: 9, No: 3, Page: 395 ~ 404, Year: 2013
10.3745/JIPS.2013.9.3.395
Keywords: adaptive classification, malicious web pages, support vector machine
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Abstract
In order to classify a web page as being benign or malicious, we designed 14 basic and 16 extended features. The basic features that we implemented were selected to represent the essential characteristics of a web page. The system heuristically combines two basic features into one extended feature in order to effectively distinguish benign and malicious pages. The support vector machine can be trained to successfully classify pages by using these features. Because more and more malicious web pages are appearing, and they change so rapidly, classifiers that are trained by old data may misclassify some new pages. To overcome this problem, we selected an adaptive support vector machine (aSVM) as a classifier. The aSVM can learn training data and can quickly learn additional training data based on the support vectors it obtained during its previous learning session. Experimental results verified that the aSVM can classify malicious web pages adaptively.

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Cite this article
IEEE Style
Young Sup Hwang, Jin Baek Kwon, Jae Chan Moon and Seong Je Cho, "Classifying Malicious Web Pages by Using an Adaptive Support Vector Machine," Journal of Information Processing Systems, vol. 9, no. 3, pp. 395~404, 2013. DOI: 10.3745/JIPS.2013.9.3.395.

ACM Style
Young Sup Hwang, Jin Baek Kwon, Jae Chan Moon and Seong Je Cho, "Classifying Malicious Web Pages by Using an Adaptive Support Vector Machine," Journal of Information Processing Systems, 9, 3, (2013), 395~404. DOI: 10.3745/JIPS.2013.9.3.395.