Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wu, KuanTing | en_US |
dc.contributor.author | Chou, ShingHua | en_US |
dc.contributor.author | Chen, ShyhWei | en_US |
dc.contributor.author | Tsai, ChingTsorng | en_US |
dc.contributor.author | Yuan, ShyanMing | en_US |
dc.date.accessioned | 2019-08-02T02:24:19Z | - |
dc.date.available | 2019-08-02T02:24:19Z | - |
dc.date.issued | 2014-01-01 | en_US |
dc.identifier.isbn | 978-1-4503-6479-9 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1145/3282373.3282407 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/152456 | - |
dc.description.abstract | Recent years the prevalence of fraudulent websites has become more severe than before. Fraudulent ecommerce websites that sell counterfeit goods not only cost financial damage to consumers but also have a great impact on Internet industry. Nowadays, there is not an effective way to confront these websites. In this paper, we look forward to achieving three goals: find the characteristics of counterfeit websites, train models for classifying ecommerce websites and provide a service to help consumers distinguish counterfeit websites from legitimate ones. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | fraudulent website | en_US |
dc.subject | counterfeit website | en_US |
dc.subject | logistic regression | en_US |
dc.subject | decision tree | en_US |
dc.subject | support vector machine | en_US |
dc.title | Application of machine learning to identify Counterfeit Website | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.doi | 10.1145/3282373.3282407 | en_US |
dc.identifier.journal | IIWAS2018: THE 20TH INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEB-BASED APPLICATIONS & SERVICES | en_US |
dc.citation.spage | 321 | en_US |
dc.citation.epage | 324 | en_US |
dc.contributor.department | 資訊工程學系 | zh_TW |
dc.contributor.department | 網路工程研究所 | zh_TW |
dc.contributor.department | Department of Computer Science | en_US |
dc.contributor.department | Institute of Network Engineering | en_US |
dc.identifier.wosnumber | WOS:000469241700046 | en_US |
dc.citation.woscount | 0 | en_US |
Appears in Collections: | Conferences Paper |