Towards an immunity based distributed algorithm to detect harmful files shared in P2P networks - Springer

摘要

Due to the free and self-organized features, the Peer-to-Peer file sharing networks have become one of the major transmission channels for harmful contents, such as child pornography and abuse video. Traditional monitoring techniques deploy centralized powerful servers at gateways to analyse and filter the P2P traffic. However, the immense amount of documents shared and transferred in the P2P networks makes these techniques quite cost-expensive and inefficient. To address this problem, we develop the iDetect, a distributed harmful content detection algorithm inspired by the Clonal Selection mechanism of immune systems. Analogous to the B-lymphocytes secreting antibodies against antigens in human bodies, the clients in the P2P networks deployed with iDetect cooperate to detect the harmful contents in a distributed and self-organized manner. We build a probability model of the detection procedure to prove the performance of iDetect theoretically. We also conduct simulations to compare iDetect with traditional centralized filtering algorithms. The theoretical proof and experimental results show that iDetect is efficient, effective, self-optimized and scalable to locate the clients sharing harmful contentsin P2P networks.