FeatNet: Large-scale Fraud Device Detection by Network Representation Learning with Rich Features

Abstract

Online fraud such as search engine poisoning, groups of fake accounts and opinion fraud is conducted by fraudsters controlling a large number of mobile devices. The key to detect such fraudulent activities is to identify devices controlled by fraudsters. Traditional approaches that fingerprint devices based on device metadata only consider single device information. However, these techniques do not utilize the relationship among different devices, which is crucial to detect fraudulent activities. In this paper, we propose an effective device fraud detection framework called FeatNet, which incorporates device features and device relationships in network representation learning. Specifically, we partition the device network into bipartite graphs and generate the neighborhoods of vertices by revised truncated random walk. Then, we generate the feature signature according to device features to learn the representation of devices. Finally, the embedding vectors of all bipartite graphs are used for fraud detection. We conduct experiments on a large-scale data set and the result shows that our approach can achieve better accuracy than existing algorithms and can be deployed in the real production environment with high performance.