Survey of Detection of Malicious Nodes in Peer to Peer

Peer-to-peer streaming has witnessed a great success, thanks to the possibility of aggregating resources from all participants. However, performance of the entire system may be highly degraded due to the presence of malicious peers that share bogus data on purpose. In a peer-to-peer (P2P) streaming system, since each peer needs to participate in uploading data to other peers, malicious peers may choose to upload bogus data so as to damage the playback and degrade the performance of normal peers in the system. This is known as pollution attack in P2P networks, and it can cause harsh impact to the performance of P2P streaming systems. There are different approaches, different use a statistical inference technique and general & fully distributed detection framework discussed in this paper, which can be executed by each legitimate node in Peer to Peer network to identify its malicious neighbors.