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The critical challenges for peer-to-peer (P2P) systems must be the task to manage risks in interacting with unknown peers. Reputation-based trust management can decrease this risk by evaluating trustworthiness of a certain peer from its historic behaviors. However, many existing trust models do not provide adequate reaction to quick changes in peersÂ¿ behavior, showing their ineffectiveness coping with dynamic malicious peers. In this paper, we propose a run-length-coding based trust model - RunTrust - computing trust degree based on behaviors history in P2P networks. In particular, our proposed model is capable to detect and penalize both the sudden changes in peersÂ¿ behaviors and their potential oscillatory malicious behavior. Simulation shows our modelÂ¿s unique features and advantages over the existing models.
Computational Intelligence and Security, 2008. CIS '08. International Conference on (Volume:1 )
Date of Conference: 13-17 Dec. 2008