DRSDetector: Detecting Gambling Websites by Multi-level Feature Fusion

With the development of the Internet, online gambling has gradually replaced the traditional way of gambling and became a popular way of making money for illegal organizations. In many countries, online gambling is prohibited by law. But in some countries, these online gambling activities can still attract a variety of victims through their secret promotion channels. In this paper, we propose a gambling website detection method called DRSDetector, which combines domain features, resource features, and semantic features. And this method uses the idea of ensemble learning to fuse different modules. Specifically, we learn the character features of domains based on two stacked Transformer Encoder structures, use the LightGBM to learn resource feature of websites, and learn the semantic feature of websites based on the HAN model. The experimental results show that the performance of DRSDetector is better than the traditional website detection methods. In addition, we also investigated the promotion channels of gambling websites and took China as an example to reveal the 10 major entertainment companies behind these websites. These will help the government to combat online gambling activities more accurately and effectively.