Node-Imbalance Learning on Heterogeneous Graph for Pirated Video Website Detection

With the rapid development of video streaming, the problem of copyright infringement has become increasingly severe. Despite its explicit illegality in many countries, a large variety of pirated video websites are still active, causing huge damage to copyright holders and security risks to users. Traditional methods for detecting malicious websites, such as blacklists or feature-based classifiers, can be easily bypassed by evading approaches like Domain-Flux. Some researchers recently proposed sophisticated graph-based methods to utilize various relations between websites and convert the detection task into node representation learning. However, the node imbalance issue impairs their performance on real-world datasets. In this paper, given the limitations of the above methods, we propose a model named Heterogeneous Graph Node Re-weighting (HGNR) to detect pirated video websites. We construct a heterogeneous graph with diverse meta relations and design a weight adjustment mechanism to deal with node imbalance issue. The experiments with different imbalance ratios show that HGNR outperforms state-of-the-art graph-based methods. Furthermore, we analyze the best-performed meta relation and disclose how video pirates gain profits, which can help the security community thwart video piracy.