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A novel reward and penalty trust evaluation model based on confidence interval using Petri Net

机译:基于Petri网的基于置信区间的新型奖罚信任评估模型

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摘要

Trust brings a novel means to improve the security of entities. Entities potentially initiate interactions with each other without having prior contacts. These interactions can either be formed directly between two entities or indirect through the recommendation of their acquaintances or third parties. In this paper, we present a novel trust model according to historical interaction between entities so that, the relations between entities are modeled based on four types (i.e. completely successful, completely unsuccessful, relatively successful and relatively unsuccessful) of their prior interactions. We also consider the reward and penalty for encouraging honest behaviors and preventing malicious behaviors, respectively. Unlike other proposed models, instead of taking into account the fixed amount of interactions for the experience level, in this paper, to calculate more accurate we have used the confidence interval to determine the level of experience. Also, to resist selfish and malicious behavior, the recommendation trust value for an entity computed by calculating the similarity-weighted recommendations of the entities that have interacted with him according to adjusted cosine similar function. In addition, we have developed the Petri Net model for design, analysis, and performance evaluation of the proposed model. By performing empirical evaluations, we have demonstrated that various scenarios can be better explained by our proposed reward and penalty trust model based on the confidence interval (RTMC) rather than the commonly used classical models. Simulation results and theoretical analysis proved that the RTMC promotes interaction between entities with containment capability in synergies cheating.
机译:信任带来了提高实体安全性的新颖方法。实体可能会在没有事先联系的情况下彼此发起交互。这些交互可以直接在两个实体之间形成,也可以通过其熟人或第三方的建议间接形成。在本文中,我们根据实体之间的历史交互关系提出了一种新颖的信任模型,因此,实体之间的关系基于其先前交互的四种类型(即完全成功,完全不成功,相对成功和相对不成功)进行建模。我们还分别考虑鼓励诚实行为和防止恶意行为的奖惩措施。与其他提出的模型不同,在本文中,为了考虑更准确的计算,我们没有使用经验水平的固定交互量,而是使用置信区间来确定经验水平。而且,为了抵制自私和恶意行为,通过根据调整后的余弦相似函数计算与实体进行交互的实体的相似度加权建议,来计算实体的建议信任值。此外,我们还开发了Petri Net模型,用于设计,分析和评估所提出模型的性能。通过执行经验评估,我们已经证明,通过我们提出的基于置信区间(RTMC)的奖励和惩罚信任模型,而不是通常使用的经典模型,可以更好地解释各种情况。仿真结果和理论分析证明,RTMC在协同作弊中促进了具有遏制能力的实体之间的相互作用。

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