基于社交网络的信任机制研究与应用

发布时间：2019-04-23 07:52

【摘要】：随着网络的发展,互联网服务成为了人们生活不可缺少的一部分,随之而来的就是更加严峻的安全问题。目前除了传统的硬安全手段之外,信任机制作为一种重要的软安全手段,得到了广泛的应用。信任关系已经成为互联网用户重要的决策依据。因此,如何建立可靠的信任关系,建立完善的信任机制是现阶段热门研究课题。本文对现在的信任机制研究进行了分析,并且选取了当前热门新兴的特殊应用--社交网络进行融合。社交网络也属于P2P网络拓扑结构,所以本文在结合P2P网络和社交网络属性的特点,将优选信任模型的构建,特别是提高信任评估的准确性、抗攻击能力和算法效率作为研究的重点,提出了一系列信任模型改进方案。本文的主要研究贡献如下:首先,针对信任值评估计算,为克服了传统信任机制中影响因素粒度过粗的问题,在信任特征选取上,综合考虑上下文的多影响因素,采用灰度关联方法,对特征间权重进行动态调节。结合基于相似性的间接信任计算,以解决节点数据贫乏的情况。最后,仿真实验结果表明本文提出的信任模型不仅具有良好的准确性,而且能够有效地抵御恶意节点的攻击。其次,研究了信任机制中的信任反馈问题。为更实时动态的调节信任值,本文采用了基于马尔科夫链预测的动态反馈算法,通过马尔科夫模型的状态确定和无后验性改进,完成基于信任惩罚激励要素的状态建立和基于时间衰减要素的状态转移,使用退火算法对调节因子给予优化,模拟出信任的预测值。最后,仿真实验表明本章算法在信任预测上具有良好的准确度,并且增强了算法的防御力和健壮性。最后,针对信任机制中的算法效率过低的问题,提出了基于节点聚类优化的算法。分析量化节点的客观属性和交互属性,构造节点的逻辑坐标,完成三维逻辑映射。根据映射的节点坐标,采用KMeans方法完成聚类。通过这种方法完成节点推荐选取。最后,仿真实验结果证明通过本章信任模型的节点选取,既保证了信任评估的准确性,又减小了算法的时间复杂度,提高了信任机制的效率。
[Abstract]:With the development of network, Internet service has become an indispensable part of people's life, followed by more serious security problems. In addition to the traditional hard security, trust mechanism, as an important soft security means, has been widely used. Trust relationship has become an important decision-making basis for Internet users. Therefore, how to establish a reliable trust relationship and establish a perfect trust mechanism is a hot research topic at this stage. In this paper, the trust mechanism research is analyzed, and the social network, a popular and emerging special application, is selected to merge. Social network also belongs to the topology of P2P network, so this paper combines the characteristics of P2P network and social network attributes, will optimize the construction of trust model, especially to improve the accuracy of trust assessment. Anti-attack ability and algorithm efficiency are the focus of the research, and a series of trust model improvement schemes are proposed. The main contributions of this paper are as follows: firstly, in order to overcome the problem of coarse influence factors in the traditional trust mechanism, the multi-influencing factors of context are comprehensively considered in the selection of trust characteristics, in order to overcome the problem of coarse influence factors in the traditional trust mechanism, aiming at the evaluation and calculation of trust values. The gray-scale correlation method is used to dynamically adjust the weights between features. The indirect trust calculation based on similarity is used to solve the problem of poor node data. Finally, the simulation results show that the trust model proposed in this paper not only has good accuracy, but also can effectively resist the attack of malicious nodes. Secondly, the problem of trust feedback in trust mechanism is studied. In order to adjust trust value in real-time and dynamically, the dynamic feedback algorithm based on Markov chain prediction is adopted in this paper, and the state determination of Markov model and the improvement of non-experientiality are adopted. The state establishment based on trust penalty incentive element and the state transition based on time attenuation factor are completed. The annealing algorithm is used to optimize the adjustment factor and to simulate the predicted value of trust. Finally, the simulation results show that this algorithm has good accuracy in the prediction of trust, and enhances the robustness and robustness of the algorithm. Finally, an algorithm based on node clustering optimization is proposed to solve the problem of low efficiency of the algorithm in trust mechanism. The objective and interactive attributes of quantized nodes are analyzed, the logical coordinates of nodes are constructed, and the three-dimensional logical mapping is completed. According to the mapped node coordinates, KMeans method is used to complete clustering. This method is used to complete the node recommendation selection. Finally, the simulation results show that the node selection of the trust model in this chapter not only ensures the accuracy of trust evaluation, but also reduces the time complexity of the algorithm, and improves the efficiency of trust mechanism.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP393.08

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