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首页> 外文期刊>Journal of supercomputing >A novel trust management scheme based on Dempster-Shafer evidence theory for malicious nodes detection in wireless sensor networks
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A novel trust management scheme based on Dempster-Shafer evidence theory for malicious nodes detection in wireless sensor networks

机译:基于Dempster-Shafer证据理论的新型信任管理方案,用于无线传感器网络中的恶意节点检测

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摘要

With the development of Internet technology, social network has become an important application in the network life. However, due to the rapid increase in the number of users, the influx of a variety of bad information is brought up as well as the existence of malicious users. Therefore, it is emergent to design a valid management scheme for user's authentication to ensure the normal operation of social networks. Node trust evaluation is an effective method to deal with typical network attacks in wireless sensor networks. In order to solve the problem of quantification and uncertainty of trust, a novel trust management scheme based on Dempster-Shafer evidence theory for malicious nodes detection is proposed in this paper. Firstly, by taking into account spatiotemporal correlation of the data collected by sensor nodes in adjacent area, the trust degree can be estimated. Secondly, according to the D-S theory, the trust model is established to count the number of interactive behavior of trust, distrust or uncertainty, further to evaluate the direct trust value and indirect trust value. Then, a flexible synthesis method is adopted to calculate the overall trust to identify the malicious nodes. The simulation results show that the proposed scheme has obvious advantages over the traditional methods in the identification of malicious node and data fusion accuracy, and can obtain good scalability.
机译:随着互联网技术的发展,社交网络已经成为网络生活中的重要应用。但是,由于用户数量的迅速增加,导致各种不良信息的涌入以及恶意用户的存在。因此,迫切需要设计一种有效的用户认证管理方案,以确保社交网络的正常运行。节点信任评估是一种应对无线传感器网络中典型网络攻击的有效方法。为了解决信任的量化和不确定性问题,提出了一种基于Dempster-Shafer证据理论的恶意节点检测信任管理方案。首先,通过考虑相邻区域中的传感器节点收集的数据的时空相关性,可以估计信任度。其次,根据D-S理论,建立信任模型,对信任,不信任或不确定性的交互行为进行计数,进而评估直接信任值和间接信任值。然后,采用一种灵活的综合方法来计算识别恶意节点的总信任度。仿真结果表明,该方案在恶意节点识别和数据融合精度方面具有优于传统方法的明显优势,并具有良好的可扩展性。

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