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 agents’ reputations are required. Reputation Management Systems (RMSs) allow agents to perform such estimation in a cooperative way. In particular, distributed RMSs exploit feedbacks provided after each interaction to predict future behaviors of agents. Such systems, are sensitive to fake information injected by malicious users, thus, predicting their performance is a very challenging task. Although many existing works have addressed some challenges concerning the design and assessment of specific RMSs, there are no simulation environments that adopt a general approach that can be applied to different application scenarios. To overcome this lack, in this work we present DRESS, an agent-based simulation framework that aims to support researchers in the evaluation of distributed RMSs under different security attacks.
|Numero di pagine||18|
|Rivista||International Journal of Intelligent Information Technologies|
|Stato di pubblicazione||Published - 2020|