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A Spam Mail Classification Using Link Structure Analysis  

Rhee, Shin-Young ((주)시뮬레이션연구소)
Khil, A-Ra (숭실대학교 컴퓨터학부)
Kim, Myung-Won (숭실대학교 컴퓨터학부)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.1, 2007 , pp. 30-39 More about this Journal
Abstract
The existing content-based spam mail filtering algorithms have difficulties in filtering spam mails when e-mails contain images but little text. In this thesis we propose an efficient spam mail classification algorithm that utilizes the link structure of e-mails. We compute the number of hyperlinks in an e-mail and the in-link frequencies of the web pages hyperlinked in the e-mail. Using these two features we classify spam mails and legitimate mails based on the decision tree trained for spam mail classification. We also suggest a hybrid system combining three different algorithms by majority voting: the link structure analysis algorithm, a modified link structure analysis algorithm, in which only the host part of the hyperlinked pages of an e-mail is used for link structure analysis, and the content-based method using SVM (support vector machines). The experimental results show that the link structure analysis algorithm slightly outperforms the existing content-based method with the accuracy of 94.8%. Moreover, the hybrid system achieves the accuracy of 97.0%, which is a significant performance improvement over the existing method.
Keywords
Spam Mail Classification; Link Structure Analysis; Decision Tree; SVM;
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  • Reference
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