Trust management method based on backward inference

Abstract

The invention discloses a trust management method based on backward inference. The trust management method based on the backward inference comprise a trust evaluation module and a trust punishment module, wherein an operation process of the trust evaluation module comprises the following steps: generating satisfaction degree evaluation among nodes through transaction among the nodes, and adopting a multiple evaluation mechanism to calculate a direct trust value of the evaluated node; utilizing a cosine similarity formula to obtain a similar node set, and calculating an indirect trust value of the evaluated node; combining the direct trust value and the indirect trust value to calculate a comprehensive trust value of the evaluated node; comparing the comprehensive trust value with a trust threshold value, and marking the evaluated node; and generating a node sequence with a mark. The operation process of the trust punishment module comprises the following steps: inquiring the comprehensive trust value for the mark node by other nodes; according to different node marks, looking for and punishing a malicious node; punishing the malicious node to lower the reputation value of the malicious node; calculating the final reputation values of all nodes; and generating a node sequence subjected to devaluation sorting according to the reputation values. The punishment mechanism is introduced in, so that an evaluation result is more accurate.

Description

technical field [0001] The invention belongs to the field of trust evaluation of nodes in a distributed system, and in particular relates to a trust management method based on reverse reasoning aimed at masquerading nodes. Background technique [0002] In recent years, distributed systems such as grid computing, ubiquitous computing, P2P computing, and Ad Hoc networks have been widely used because of their advantages such as high reliability, small transmission delay, and easy resource sharing among nodes in the network. Compared with the centralized system, distributed is conducive to the distribution and optimization of tasks on the entire computer system, and overcomes the defects that the traditional centralized system will cause the resource shortage and response bottleneck of the central host. On the other hand, a distributed system stores data in multiple independent machines without a central node, but its topology is more complex, so its security issues are more pro...

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Problems solved by technology

This method adopts a multi-evaluation mechanism, evaluates the trust degree of nodes through fuzzy filtering, and reduces the probability of node whitewashing, but does not mention the punishment mechanism for malicious nodes

3) "Sensor Network Trust Evaluation Method Based on Node Behavior and D-S Evidence Theory (CN101835158B)", the design of node trust factor strategy in wireless sensor network, using the membership degree of fuzzy set theory to ensure the security of data transmission, but it is different from this The method proposed by the patent is not associated with

This method improves the evaluation system that the higher the price, the higher the trust, and prevents malicious entities from making credit speculation through high-priced commodities, but this method does not consider disguised entities

3) Zhang Lin et al. (Zhang Lin, Liu Jingwen, Wang Ruchuan, et al. A trust evaluation model based on improved D-S evidence theory [J]. Journal of Communications, 2013, 34(7): 167-173.) proposed a The evaluation method calculates the trust value of the evaluation object from the evidence sequence about the evaluation object obtained from the local evidence base and the indirect information obtained from the recommender set to prevent malicious nodes from gathering trust attacks. malicious node

Whether the trust management system can effectively identify fake nodes is an important indicator of the evaluation model, but most of the existing trust models are effective for ordinary malicious nodes, and the identification degree of fake nodes is poor

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