Cited by 256Access Control and TrustIoT and Edge/Fog ComputingInternet of Things and AI

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Machine Learning Based Trust Computational Model for IoT Services

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Jayasinghe, Upul, Lee, Gyu Myoung, Um, Tai-Won, Shi, Qi·2019·IEEE Transactions on Sustainable Computing

DOI 10.1109/tsusc.2018.2839623

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Abstract

The Internet of Things has facilitated access to a large volume of sensitive information on each participating object in an ecosystem. This imposes many threats ranging from the risks of data management to the potential discrimination enabled by data analytics over delicate information such as locations, interests, and activities. To address these issues, the concept of trust is introduced as an important role in supporting both humans and services to overcome the perception of uncertainty and risks before making any decisions. However, establishing trust in a cyber world is a challenging task due to the volume of diversified influential factors from cyber-physical-systems. Hence, it is essential to have an intelligent trust computation model that is capable of generating accurate and intuitive trust values for prospective actors. Therefore, in this paper, a quantifiable trust assessment model is proposed. Built on this model, individual trust attributes are then calculated numerically. Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making. Finally, our model's effectiveness is verified through a simulation. The results show that our method has advantages over other aggregation methods.

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Authors: Jayasinghe, U., Lee, G. M., Um, T.-W., & Shi, Q.
Affiliations: Liverpool John Moores University (GB)
Chosun University (KR)
ORCID: Jayasinghe, Upul
Lee, Gyu Myoung
Um, Tai-Won
Shi, Qi
Venue: IEEE Transactions on Sustainable Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-01-01
Volume: 4
Issue: 1
Pages: 39-52
ISSN: 2377-3782 (ISSN-L)
2377-3790
DOI: 10.1109/tsusc.2018.2839623
OpenAlex: W2804907169
Cited by: 256
References: 49
Citations by year: 2026: 19 · 2025: 26 · 2024: 44 · 2023: 45 · 2022: 39 · 2021: 41 · 2020: 29 · 2019: 10
FWCI: 47.23
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations: IEEE Transactions on Sustainable Computing (publishedVersion)
Liverpool John Moores University (acceptedVersion)
Funders: National Research Foundation of Korea
Ministry of Science and ICT (2018R1A2B2003774)
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › Access Control and Trust
Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords: trust computation, Internet of Things services, trust aggregation, decision-making, cyber-physical systems, privacy risks, data discrimination

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Machine Learning Based Trust Computational Model for IoT Services

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