Open Access

ARTICLE

A Trust Evaluation Mechanism Based on Autoencoder Clustering Algorithm for Edge Device Access of IoT

by Xiao Feng^1,2,3,*, Zheng Yuan¹

1 Cyber Security Academy, Beijing University of Posts and Telecommunications, Beijing, 100876, China
2 Department of Cyberspace Security, Beijing Electronic Science and Technology Institute, Beijing, 100070, China
3 State Grid Info-Telecom Great Power Science and Technology Co., Ltd., Beijing, 102211, China

* Corresponding Author: Xiao Feng. Email:

Computers, Materials & Continua 2024, 78(2), 1881-1895. https://doi.org/10.32604/cmc.2023.047243

Received 30 October 2023; Accepted 19 December 2023; Issue published 27 February 2024

Abstract

First, we propose a cross-domain authentication architecture based on trust evaluation mechanism, including registration, certificate issuance, and cross-domain authentication processes. A direct trust evaluation mechanism based on the time decay factor is proposed, taking into account the influence of historical interaction records. We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data. We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record. Then we propose an autoencoder-based trust clustering algorithm. We perform feature extraction based on autoencoders. Kullback leibler (KL) divergence is used to calculate the reconstruction error. When constructing a convolutional autoencoder, we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder. The sparse penalty term in the loss function measures the difference through the KL divergence. Trust clustering is performed based on the density based spatial clustering of applications with noise (DBSCAN) clustering algorithm. During the clustering process, edge nodes have a variety of trustworthy attribute characteristics. We assign different attribute weights according to the relative importance of each attribute in the clustering process, and a larger weight means that the attribute occupies a greater weight in the calculation of distance. Finally, we introduced adaptive weights to calculate comprehensive trust evaluation. Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.

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Keywords

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