The Journal of Supercomputing

, Volume 74, Issue 4, pp 1779–1801 | Cite as

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

Article

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.

Keywords

Trust management scheme Dempster–Shafer evidence theory Security Wireless sensor networks 

Notes

Acknowledgements

This work was supported by Key Technologies Research and Development Program of China (2013BAH16F02), National Natural Science Foundation of China (61309029). We wish to thank the anonymous reviewers who helped to improve the quality of the paper. The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Wei Zhang
    • 1
  • Shiwei Zhu
    • 1
  • Jian Tang
    • 1
  • Naixue Xiong
    • 2
  1. 1.School of InformationCentral University of Finance and EconomicsBeijingChina
  2. 2.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

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