A multi-dimensional trust attestation solution in 5G-IoT

Abstract

The core missions of IoT are to sense data, transmit data and give feedback to the real world based on the calculation of the sensed data. The trust of sensing source data and transmission network is extremely important to IoT security. 5G-IoT with its low latency, wide connectivity and high-speed transmission extends the business scenarios of IoT, yet it also brings new challenges to trust proof solutions of IoT. Currently, there is a lack of efficient and reliable trust proof solutions for massive dynamically connected nodes, while the existing solutions have high computational complexity and can't adapt to time-sensitive services in 5G-IoT scenarios. In order to solve the above problems, this paper proposes an adaptive multi-dimensional trust proof solution. Firstly, the static and dynamic attributes of sensing nodes are metricized, and the historical interaction as well as the recommendation information are combined with the comprehensive metric of sensing nodes, and a multi-dimensional fine-grained trusted metric model is established in this paper. Then, based on the comprehensive metrics, the sensing nodes are logically grouped and assigned with service levels to achieve the screening and isolation of malicious nodes. At the same time, the proposed solution reduces the energy consumption of the metric process and optimizes the impact of real-time metrics on the interaction latency. Simulation experiments show that the solution can accurately and efficiently identify malicious nodes and effectively guarantee the safe and trustworthy operation of 5G-IoT nodes, while having a small impact on the latency of the 5G network.

Keywords

5G-IoT / Trusted metrics / Trust model

Declaration of Competing Interest

The authors declared that they have no conflicts of interest to this work.

Acknowledgements

This work is supported by National Key R&D Program of China (2019YFB2102303), National Natural Science Foundation of China (NSFC61971014, NSFC11675199), Beijing Postdoctoral Research Foundation (2021-ZZ-079), Young Backbone Teacher Training Program of Henan Colleges and Universities (2021GGJS170) and Henan Province Higher Education Key Research Project (23B520014).

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