“…We also plan to investigate dynamic and self-adaptive monitoring intervals which could be increased or decreased in runtime based on the system load. presents a model and estimates content pollution propagation [11][12][13] ✓ employs band codes to construct black lists of polluters [47] ✓ network coding is employed to allow the identification of polluters and limit content pollution [32] ✓ proposes the application of black lists to live streaming [17,27] ✓ transmits hash-based signatures of all chunks; the challenge is to receive hashes in advance for live streaming [21,24] ✓ applies cryptography to every whole chunk and employs a distributed key management scheme [36] ✓ employs peer groups to ensure chunk integrity; the server publishes content information in that group; peers access the group to verify chunks integrity [34] ✓ Merkle-trees are employed that use hashes to guarantee the integrity of streams of chunks [35] presents an evaluation of black lists, cryptography, hash-based verification and digital signatures [38] presents an evaluation of the impact of pollution attacks; shows that the impact depends on the stability of the network, and on the bandwidth available at both malicious peers and the source [37] evaluates content authentication mechanisms to live streaming [39,40] ✓ employs reputation and ranking to file sharing P2P systems [22] ✓ employs reputation and ranking to live streaming; the reputation mechanisms are based on peer experience [23] shows that reputation-based approaches can suffer with the collusion of malicious peers, with false positives, and present a high delay to propagate conclusions [41] a confidence management strategy is proposed based on retransmissions of the polluted data; depending on the situation the number of retransmissions can be high [42] in order to prevent DDoS attacks on streming sources proposes a strategy to hide source identity; used in the context of P2P VoD [43] presents a through evaluation of SopCast reaches the conclusion that a single attacker can harm up to 50% of peers and consume up to 30% of the available bandwidth [44] a centralised (non-distributed) solution is proposed to detect polluters in live streaming networks employing comparasionbased system-level diagnosis [this work] ✓ presents a distributed strategy that employs comparison-based diagnosis to combat pollution in live streaming; each peer independently identifies and stops requesting chunks from its polluter neighbours…”