Name
Title | ||
|---|---|---|
Recognizing roles of online illegal gambling participants: An ensemble learning approach. |
Abstract | ||
|---|---|---|
•We present an automatic PRR approach which learns a supervised classifier based on monetary transaction data to predict the roles of IOG participants. To the best of our knowledge, this is the first computational intelligence based technique to tackle PRR problem in an IOG ecosystem.•We propose two sets of features, i.e. transaction statistical features and network structural features, to effectively represent participants. These features can capture both behavior patterns and structural importance of participants.•We adopt an ensemble learning strategy in the training phase of the PRR classifier to reduce the impact of unbalanced training data.•We evaluate the performance of the proposed approach using real-world IOG monetary transaction data. Experimental results demonstrate the feasibility and validity of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cose.2019.101588 | Computers & Security |
Keywords | Field | DocType |
Online gambling,Role recognition,Cybercrime,Monetary transaction,Ensemble learning | Training set,Online gambling,Internet privacy,Prosperity,Social issues,Computer science,Classifier (linguistics),Database transaction,Ensemble learning,Transaction data | Journal |
Volume | ISSN | Citations |
87 | 0167-4048 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohui Han | 1 | 17 | 5.41 |
| Lianhai Wang | 2 | 41 | 10.98 |
| Shujiang Xu | 3 | 2 | 1.71 |
| Dawei Zhao | 4 | 193 | 20.38 |
| Guangqi Liu | 5 | 1 | 0.35 |