Developing Effective Fraud Detection Methods for Online Auction

淡江大學機構典藏 > 商管學院 > 企業管理學系暨研究所 > 會議論文 > Item 987654321/120741

請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120741

題名:	Developing Effective Fraud Detection Methods for Online Auction
作者:	張昭憲;劉祐宏;李青芬
關鍵詞:	fraud detection;feature selection;classification, online auction;e-commerce
日期:	2020-10-28
上傳時間:	2021-05-05 12:13:58 (UTC+8)
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.
關聯:	TANET 2020 台灣網際網路研討會論文集
顯示於類別:	[企業管理學系暨研究所] 會議論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
Developing Effective Fraud Detection Methods for Online Auction.pdf		932Kb	Adobe PDF	127	檢視/開啟
index.html		0Kb	HTML	68	檢視/開啟

在機構典藏中所有的資料項目都受到原著作權保護.

TAIR相關文章