ART: Active recognition trust mechanism for Augmented Intelligence of Things (AIoT) in smart enterprise systems

In smart enterprise systems, augmented IoT can efficiently improve the decision-making, handling, and generation of a huge amount of information during communication. However, Augmented Internet-of-Things (AIoT) leads to various security and trust issues when transmitting information through interme...

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Bibliographic Details
Main Authors: Geetanjali Rathee, Akshay Kumar, Sahil Garg, Bong Jun Choi, Mohammad Mehedi Hassan
Format: Article
Language:English
Published: Elsevier 2023-10-01
Series:Alexandria Engineering Journal
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016823007287
Description
Summary:In smart enterprise systems, augmented IoT can efficiently improve the decision-making, handling, and generation of a huge amount of information during communication. However, Augmented Internet-of-Things (AIoT) leads to various security and trust issues when transmitting information through intermediate devices. In a case where malicious devices can easily integrate with legitimate devices, it can further affect and interfere with the overall performance of the network system. Though various security surveys have been illustrated and schemes have been proposed by scientists, however, all of them are in their early stages. This paper proposes a trusted decision-making mechanism called Active Recognition Trust (ART), using AIoT for handling smart enterprise systems. The proposed mechanism integrates active recognition and associated reference mechanisms to improve the efficiency and effectiveness of the secure transmission process by computing a trust value for each device using impact factors of function fusion systems before information exchanges. Simulation results show that the proposed mechanism can efficiently enhance performance while improving the accuracy of recognizing legitimate devices by reducing or eliminating interference from malicious devices. The proposed mechanism is evaluated using the transmission ratio, identification accuracy, average trust, and run cycle compared to the existing mechanisms. Further, the proposed mechanism achieves approximately 89% better improvement than the baseline approach.
ISSN:1110-0168