An ensemble approach applied to classify spam e-mails

Authors:
Kuo-Ching Ying;Shih-Wei Lin;Zne-Jung Lee;Yen-Tim Lin
Affiliations:
Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan;Department of Information Management, Chang Gung University, No. 259, Wen-Hwa 1st Road, Tao-Yuan, Taiwan;Department of Information Management, Huafan University, No. 1, Huafan Rd., Shihding Township, Taipei County 22301, Taiwan;Department of Information Management, Huafan University, No. 1, Huafan Rd., Shihding Township, Taipei County 22301, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Spam e-mails, known as unsolicited e-mail messages, have become an increasing problem for information security. The intrusion of spam e-mails persecute the users and waste the network resources. Traditionally, machine learning and statistical filtering systems are used to filter out spam e-mails. However, there is no unique method can be successfully applied to classify spam e-mails. It is necessary to apply multiple approaches to detect spam and effectively filter out the increasing volumes of spam e-mails. In this paper, an ensemble approach, based on decision tree, support vector machine and back-propagation network, is applied to classify spam e-mails. The proposed approach is based on the characteristics of the spam e-mails. The spam e-mails are categorized into 14 features and then the ensemble approach is performed to classify them. From simulation results, the proposed ensemble approach outperforms other approaches for two test datasets.