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

Zhao Li, Shijun Zhang, Jiang Yin, Meijie Du, Zhongyi Zhang, Qingyun Liu
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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.
打击盗版:第三方服务增强的盗版视频网站检测方法
随着视频流媒体的发展,盗版视频网站的不断增多给版权方带来了前所未有的损失,也给用户带来了潜在的安全隐患。尽管人们已经采取了很多措施来打击盗版视频网站,但它们仍在利用Fast-Flux域名和网络犯罪即服务(CaaS)工具等规避手段出现。为了检测盗版视频网站,本文提出了一种第三方增强盗版视频网站分类网络(TEP-Net),该网络集成了网站及其第三方服务之间的语义特征和关系信息。更具体地说,我们使用CNN-BiLSTM-Attention来探索字符级和领域级文本嵌入,并通过在分类中构造统计特征来利用关系信息。实验表明,与现有方法相比,TEP-Net取得了显著的性能。此外,我们对盗版视频网站背后的CaaS进行了深入分析。我们的研究可以帮助安全社区更准确、更有效地打击视频盗版。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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