基于双重属性值的分组P2P信任模型  

Grouping P2P Trust Model Based on Dual Attribute Values

作　　者：曹晓梅[1,2] 邵幸海 陆子南 

机构地区：[1]南京邮电大学计算机与软件学院,南京210003 [2]江苏省无线传感网高技术研究重点实验室,南京210003

出　　处：《计算机工程》2015年第3期130-135,共6页Computer Engineering

摘　　要：为提高P2P信任模型对恶意节点的抑制能力,提出一种改进的分组P2P信任模型。利用模糊推理规则结合信任值和贡献值,将网络中节点划分为若干不同等级的小组,通过小组等级限制节点的资源访问权限。在直接信任度的计算中引入时间衰减函数反映节点的实时情况,并设置惩罚因子对节点的恶意行为进行惩罚。在推荐信任度的计算中结合小组等级计算推荐节点可信度,以降低算法的复杂度。数据分析结果表明,该模型能有效抑制恶意节点的攻击,随着共谋节点、自私节点及震荡节点的增加,其文件下载成功率高于Peer Trust模型和Eigen Trust模型。To improve the P2P trust model＇s ability of inhibiting malicious nodes. Grouping nodes in the network according to the level by using the basic fuzzy inference rule and combining trust value and contribution value,and limiting the resource access through the level of the node. Time attenuation function reflecting the actual situation is introduced and penalty factor punishing malicious behavior of the node in the calculation of the comprehensive trust is designed. In the calculation of recommend trust,it effectively reduces the complexity of the algorithm by using the recommend node＇s credibility which is calculated by the level of node. According to the analysis of data,the proposed model can effectively inhibit the malicious nodes attack,with the increasing proportion of collusion nodes,free-rider nodes and concussion nodes in the network,file download successful rate of this model is higher than Peer Trust model and Eigen Trust model.

关 键 词：信任模型 双重属性值 分组 时间衰减函数 惩罚因子 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]