Distributed environments consist of a huge number of entities that cooperate to achieve complex goals. When interactions occur between unknown parties, intelligent techniques for estimating agent reputations are required. Reputation management systems (RMS's) allow agents to perform such estimation in a cooperative way. In particular, distributed RMS's exploit feedbacks provided after each interaction and allow prediction of future behaviors of agents. Such systems, in contrast to centralized RMSs, are sensitive to fake information injected by malicious users; thus, predicting the performance of a distributed RMS is a very challenging task. Although many existing works have addressed some challenges concerning the design and assessment of specific RMS's, there are no simulation environments that adopt a general approach that can be applied to different application scenarios. To overcome this lack, we present DRESS, an agent-based simulation framework that aims to support researchers in the evaluation of distributed RMSs under different security attacks.
A Collusion-Resistant Distributed Agent-Based Signature Delegation (CDASD) Protocol for E-Commerce Applications
