教師資料查詢 | 類別: 會議論文 | 教師: 李青芬 LEE, CHING-FEN (瀏覽個人網頁)

標題:Developing Effective Fraud Detection Methods for Online Auction
學年
學期
發表日期2020/10/28
作品名稱Developing Effective Fraud Detection Methods for Online Auction
作品名稱(其他語言)
著者張昭憲; 劉祐宏; 李青芬
作品所屬單位
出版者
會議名稱TANET 2020 台灣網際網路研討會
會議地點台北市台灣
摘要The past decade has witnessed the rapid growth of online auctions. However, the low cost and anonymity in joining online auctions provided an easy path for fraudsters. The simple binary reputation system promoted by the auction site is clearly not enough to protect consumers from fraud. In view of this, many fraud detection methods have been proposed. Nevertheless, there are still many weaknesses needed to be improved. To help secure the online trading environment, this study aims at developing more effective methods to identify the fraudsters in online auctions. First, a novel selection method is proposed for deriving a concise attribute set used to build efficient detection models, which allow a reduction in detection costs while improving detection accuracy. In addition, a two-stage detection procedure is proposed wherein multiple mutual-complement models are combined for promoting overall detection accuracy. To evaluate the proposed methods, actual auction transaction histories were collected for testing. The experimental results show that these methods can outperform those in the previous work.
關鍵字fraud detection;feature selection;classification, online auction;e-commerce
語言中文
收錄於
會議性質國內
校內研討會地點
研討會時間20201028~20201028
通訊作者
國別中華民國
公開徵稿
出版型式
出處TANET 2020 台灣網際網路研討會論文集
相關連結
SDGs
  • 產業創新與基礎設施
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