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Erschienen in: The Journal of Supercomputing 4/2018

26.09.2017

A novel trust management scheme based on Dempster–Shafer evidence theory for malicious nodes detection in wireless sensor networks

verfasst von: Wei Zhang, Shiwei Zhu, Jian Tang, Naixue Xiong

Erschienen in: The Journal of Supercomputing | Ausgabe 4/2018

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Abstract

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.

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Metadaten
Titel
A novel trust management scheme based on Dempster–Shafer evidence theory for malicious nodes detection in wireless sensor networks
verfasst von
Wei Zhang
Shiwei Zhu
Jian Tang
Naixue Xiong
Publikationsdatum
26.09.2017
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 4/2018
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-017-2150-3

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