| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 102 === Trust is one of the important issues related to unknown networks. A mechanism which can distinguish a trustworthy node from an untrustworthy one is essential. The effectiveness of the mechanism depends on the accuracy of node’s reputation. Dynamics of Trust often happens in a trusted network. It causes intoxication and disguise for nodes, resulting in abnormal behaviors. This thesis proposes a semi-distributed reputation mechanism based on Dynamic Data-Driven Application System. This mechanism includes two reputations: Local Reputation (LRep) and Global Reputation (GRep). Nodes use their own experience and neighbors’ recommendations to compute LRep, which is then used to determine whether to continue trading or not. LReps are uploaded to the central controller. The central controller computes GRep, which can then be used to determine whether to continue trading. Neural Network is used in the experiments. The experimental results show that a GRep can be computed with only on average 52.21% LReps uploaded. Also, GRep rises or falls on average 26.5% in a short period of time. This phenomenon demonstrates the proposed mechanism can effectively handle Dynamics of Trust.
|
|---|
