Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

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

Authors
Rathee, GeetanjaliKumar, AkshayGarg, SahilChoi, Bong JunHassan, Mohammad Mehedi
Issue Date
Oct-2023
Publisher
ELSEVIER
Keywords
Smart enterprises; AIoT; Augmented systems; Secure augmented; Intelligent systems
Citation
ALEXANDRIA ENGINEERING JOURNAL, v.80, pp.417 - 424
Journal Title
ALEXANDRIA ENGINEERING JOURNAL
Volume
80
Start Page
417
End Page
424
URI
https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/44538
DOI
10.1016/j.aej.2023.08.043
ISSN
1110-0168
Abstract
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.
Files in This Item
Go to Link
Appears in
Collections
College of Information Technology > School of Computer Science and Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Choi, Bong Jun photo

Choi, Bong Jun
College of Information Technology (School of Computer Science and Engineering)
Read more

Altmetrics

Total Views & Downloads

BROWSE