Name
Abstract | ||
|---|---|---|
App markets play an important role in distributing various apps to mobile users. The app market vendors provide reputation systems to assist users in finding useful and reputable apps by ranking them. Unfortunately, there are signs that an app's ranking can be easily manipulated, which causes unfair competition for those highly ranked ones. Here we propose a novel approach based on deep learning to detect such malicious ranking manipulations. The proposed neural network has a novel architecture that is able to incorporate a variety of features designed from the publicly available application information in the app market. We have conducted extensive experiments as well as individual case analysis and the results demonstrate the effectiveness of our proposed approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MWC.2019.1800322 | IEEE Wireless Communications |
Field | DocType | Volume |
Architecture,World Wide Web,Unfair competition,Ranking,Computer science,Computer network,Artificial intelligence,Deep learning,Artificial neural network,Reputation,Case analysis | Journal | 26 |
Issue | ISSN | Citations |
4 | 1536-1284 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daojing He | 1 | 1013 | 58.40 |
| Kai Hong | 2 | 0 | 0.34 |
| Yao Cheng | 3 | 1 | 0.70 |
| Zongli Tang | 4 | 0 | 0.34 |
| Mohsen Guizani | 5 | 6456 | 557.44 |