Fighting Against Piracy:An Approach to Detect Pirated Video Websites Enhanced by Third-party Services

Abstract

Along with the development of video streaming, the increasing number of pirated video websites has caused unprecedented damage to copyright holders and potential security risks to their users. Though many efforts have been made to take down pirated video websites, they are still emerging by utilizing evading approaches like Fast-Flux domains and Cybercrime-as-a-Service(CaaS) tools. In this paper, to detect pirated video websites, we propose a Third-party Enhanced Pirated Video Website Classification Network (TEP-Net), which integrates both semantic features and relationship information between websites and their third-party services. More specifically, we apply CNN-BiLSTM-Attention to explore both character-level and domain-level textual embedding and utilize relationship information by constructing statistical features in classification. The experiment shows that TEP-Net achieves a significant performance compared with existing methods. Furthermore, we perform an in-depth analysis of the CaaS behind pirated video websites. Our research can help the security community fight against video piracy more precisely and effectively.