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Network coding has been shown to be capable of greatly improving quality of service in P2P live streaming systems (e.g., IPTV). However, network coding is vulnerable to pollution attacks where malicious nodes inject into the network bogus data blocks that are combined with other legitimate blocks at downstream nodes, leading to incapability of decoding the original blocks and substantial degradation of network performance. In this paper, we propose a novel approach to limiting pollution attacks by rapidly identifying malicious nodes. Our scheme can fully satisfy the requirements of live streaming systems, and achieves much higher efficiency than previous schemes. Each node in our scheme only needs to perform several hash computations for an incoming block, incurring very small computational latency. The space overhead added to each block is only 20 bytes. The verification information given to each node is independent of the streaming content and thus does not need to be redistributed. The simulation results based on real PPLive channel overlays show that the process of identifying malicious nodes only takes a few seconds even in the presence of a large number of malicious nodes.

This paper presents results from our measurement and modeling efforts on the large-scale peerto-peer (p2p) overlay graphs spanned by the PPLive system, the most popular and largest p2p IPTV (Internet Protocol Television) system today. Unlike other previous studies on PPLive, which focused on either network-centric or user-centric measurements of the system, our study is unique in (a) focusing on PPLive overlay-specific characteristics, and (b) being the first to derive mathematical models for its distributions of node degree, session length, and peer participation in simultaneous overlays.Our studies reveal characteristics of multimedia streaming p2p overlays that are markedly different from existing file-sharing p2p overlays. Specifically, we find that: (1) PPLive overlays are similar to random graphs in structure and thus more robust and resilient to the massive failure of nodes, (2) Average degree of a peer in the overlay is independent of the channel population size and the node degree distribution can be fitted by a piecewise function, (3) The availability correlation between PPLive peer pairs is bimodal, i.e., some pairs have highly correlated availability, while others have no correlation, (4) Unlike p2p file-sharing peers, PPLive peers are impatient and session lengths (discretized, per channel) are typically geometrically distributed, (5) Channel population size is time-sensitive, self-repeated, event-dependent, and varies more than in p2p filesharing networks, (6) Peering relationships are slightly locality-aware, and (7) Peer participation in simultaneous overlays follows a Zipf distribution. We believe that our findings can be used to understand current large-scale p2p streaming systems for future planning of resource usage, and to provide useful and practical hints for future design of large-scale p2p streaming systems.

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