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Title: An intelligent three-phase spam filtering method based on decision tree data mining
Authors: 許志堅
Sheu, Jyh-Jian
Chen, Yin-Kai
Chu, Ko-Tsung
Tang, Jih-Hsin
Yang, Wei-Pang
Contributors: 廣播電視學系
Keywords: Artificial intelligence;Decision trees;Electronic mail;Internet;Learning systems;Supervised learning;Trees (mathematics);Filtering method;Learning mechanism;Operating efficiency;Overall accuracies;Spam;Spam filtering;Supervised machine learning;Three phase;Data mining
Date: 2016-11
Issue Date: 2017-08-23 11:36:00 (UTC+8)
Abstract: In this paper, we proposed an efficient spam filtering method based on decision tree data mining technique, analyzed the association rules about spams, and applied these rules to develop a systematized spam filtering method. Our method possessed the following three major superiorities: (i) checking only an e-mail's header section to avoid the low-operating efficiency in scanning an e-mail's content. Moreover, the accuracy of filtering was enhanced simultaneously. (ii) In order that the probable misjudgment in identifying an unknown e-mail could be “reversed”, we had constructed a reversing mechanism to help the classification of unknown e-mails. Thus, the overall accuracy of our filtering method will be increased. (iii) Our method was equipped with a re-learning mechanism, which utilized the supervised machine learning method to collect and analyze each misjudged e-mail. Therefore, the revision information learned from the analysis of misjudged e-mails incrementally gave feedback to our method, and its ability of identifying spams would be improved.
Relation: Security and Communication Networks, 9(17), 4013-4026
Data Type: article
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Appears in Collections:[廣播電視學系] 期刊論文

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