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政大典藏 > College of Communication > Department of Radio & Television & Graduate Program > Periodical Articles > Item 140.119/112126

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/112126

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
DOI link:	http://dx.doi.org/10.1002/sec.1584
DOI:	10.1002/sec.1584
Appears in Collections:	[Department of Radio & Television & Graduate Program] Periodical Articles

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