Name
Abstract | ||
|---|---|---|
Fraud control is important for the online marketplace. This study addresses the problem of detecting attempts to deceive orders in Internet transactions. Our goal is to generate an algorithm model to detect and prevent the fraudulent orders. First, after analyzing the real historical data of customers' orders from Dangdang Website (http://www.dangdang.com. E-commerce China Dangdang Inc (Dangdang) is a leading e-commerce company in China. Dangdang officially listed on the New York Stock Exchange on December 8th, 2010, and is the first Chinese B2C e-commerce company which is completely based on online business to list on New York Stock.), we described characteristics related to transactions that may indicate frauds orders. We presented fraudulent orders characteristic matrix through comparing the normal and abnormal orders. Secondly, we apply Logic Regression model to identify frauds based on the characteristic matrix. We used real data from Dang company to train and evaluate our methods. Finally we evaluated the validity of solutions though analyzing feedback data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-16050-4_9 | Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering |
Keywords | Field | DocType |
Internet translation,Fraud order,Big data,Fraud detection,Fraud prevention,Logistic regression | Characteristic matrix,Computer science,Computer security,Stock exchange,Database transaction,Big data,Online business,The Internet | Conference |
Volume | ISSN | Citations |
142 | 1867-8211 | 1 |
PageRank | References | Authors |
0.36 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinghong Yang | 1 | 1 | 0.70 |
| Xiangquan Hu | 2 | 1 | 0.70 |
| Zhichao Cheng | 3 | 2 | 2.06 |
| Kang Miao | 4 | 1 | 0.70 |
| Xiaohong Zheng | 5 | 1 | 0.36 |