Title: Abnormal node detection method for industrial internet of things based on dynamic trust evaluation algorithm

Authors: Bin Cai

Addresses: Jiangsu Vocational College of Electronics and Information, Huai'an, 223000, China

Abstract: In order to accurately detect abnormal nodes, an industrial internet of things (IIoT) abnormal node detection method based on dynamic trust evaluation algorithm is proposed. Dynamic trust evaluation considers direct trust, recommendation trust, and historical behavioural trust. This evaluation establishes the trustworthiness of each node in the IIoT. Features are extracted from nodes within the positioning range based on their correlations. These features help identify abnormal nodes accurately. Weighted tracking tasks analyse the trust level of each node and collected data to identify anomalies. Detection results are compared with actual outcomes. Test results show accurate node trust evaluation and consistent detection of abnormal nodes. Implementing this method enhances IIoT's security and reliability by efficiently identifying and responding to abnormal nodes.

Keywords: dynamic trust evaluation algorithm; industrial internet of things; IIoT; abnormal node detection; node characteristics.

DOI: 10.1504/IJMTM.2025.145934

International Journal of Manufacturing Technology and Management, 2025 Vol.39 No.3/4/5, pp.287 - 299

Received: 27 Dec 2023
Accepted: 03 Jun 2024
Published online: 30 Apr 2025 *

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