| dc.contributor | 廣播電視學系 | |
| dc.creator (作者) | 許志堅 | zh_tw |
| dc.creator (作者) | Sheu, Jyh-Jian | en_US |
| dc.creator (作者) | Chen, Yin-Kai | en_US |
| dc.creator (作者) | Chu, Ko-Tsung | en_US |
| dc.creator (作者) | Tang, Jih-Hsin | en_US |
| dc.creator (作者) | Yang, Wei-Pang | en_US |
| dc.date (日期) | 2016-11 | |
| dc.date.accessioned | 23-Aug-2017 11:36:00 (UTC+8) | - |
| dc.date.available | 23-Aug-2017 11:36:00 (UTC+8) | - |
| dc.date.issued (上傳時間) | 23-Aug-2017 11:36:00 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/112126 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 98 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Security and Communication Networks, 9(17), 4013-4026 | |
| dc.subject (關鍵詞) | 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 | |
| dc.title (題名) | An intelligent three-phase spam filtering method based on decision tree data mining | en_US |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1002/sec.1584 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1002/sec.1584 | |
