MeshTrust: A CDN-Centric Trust Model for Reputation Management on Video Traffic
| Autor: | Yong Sun, Chao Zheng, Xiang Tian, Yujia Zhu, Qingyun Liu, Zhao Li |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Structure (mathematical logic)
business.industry Computer science media_common.quotation_subject 020206 networking & telecommunications Cloud computing 02 engineering and technology Service provider 020204 information systems 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) business Lattice graph Representation (mathematics) Reputation management Reputation media_common Computer network |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030227401 ICCS (2) |
| Popis: | Video applications today are more often deploying content delivery networks (CDNs) for content delivery. However, by decoupling the owner of the content and the organization serving it, CDNs could be abused by attackers to commit network crimes. Traditional flow-level measurements for generating reputation of IPs and domain names for video applications are insufficient. In this paper, we present MeshTrust, a novel approach that assessing reputation of service providers on video traffic automatically. We tackle the challenge from two aspects: the multi-tenancy structure representation and CDN-centric trust model. First, by mining behavioral and semantic characteristics, a Mesh Graph consisting of video websites, CDN nodes and their relations is constructed. Second, we introduce a novel CDN-centric trust model which transforms Mesh Graph into Trust Graph based on extended network embedding methods. Based on the labeled nodes in Trust Graph, a reputation score can be easily calculated and applied to real-time reputation management on video traffic. Our experiments show that MeshTrust can differentiate normal and illegal video websites with accuracy approximately 95% in a real cloud environment. |
| Databáze: | OpenAIRE |
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