可信网络中一种考虑不确定性的节点信誉量化模型

可信网络中节点信誉是一种重要而带有不确定性的信任关系。信誉及其聚合过程具有模糊性和随机性特征。云模型能够科学地描述节点信誉及其形成过程的不确定性。采用逆向云生成算法，揭示了节点信誉及其聚合过程在整个生命期的模糊性和随机性规律;获得其信誉云的数字特征值，指导在局部窗口内其信誉的量化。基于服务满意度评价的确定度值这一具有稳定倾向的随机数，结合衰减系数，设计了信誉度量化模型。模型中的权值是与满意度值有关的有稳定倾向的随机数。所提信誉量化模型切合开放网络信任关系的不确定性规律。仿真结果表明，与其它信誉量化模型相比，所提出的模型计算结果稳定，且具有较强的杭攻击性。

Quantification Model Considering Uncertainties for Node Reputation in Trusted Networl}s

Node reputation in trusted networks is an important trust relationship with many uncertainties. Reputation and the process of its aggregation arc companied with characteristics of fuzziness and randomness. Cloud model can scientifically describe uncertainties of reputation during the process of its aggregation. Converse cloud generation algorithm is adopted to discover the laws of fuzziness and randomness of node reputation and its aggregation process during its whole life. Obtained numeric eigenvalues of reputation cloud are to direct the quantification of node reputation in local computation widows. Based on certainty degree of service satisfaction degree, together with attenuation factor, a reputation quantifaction model was proposed. Certainty degree is a random number with stable tendency. Weights in the model are also random numbers with stable tendency related to service satisfaction degrees. The proposed reputation quantifaction model well fits with uncertainty laws of trust relationship in open networks. Simulation results show that the proposed model maintains stable output and has good anti-attacking abilities, compared with other models.