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Machine Learning based Trust Computational Model for IoT Services

Jayasinghe, U, Lee, GM, Um, TW and Shi, Q (2018) Machine Learning based Trust Computational Model for IoT Services. IEEE Transactions on Sustainable Computing, 4 (1). pp. 39-52. ISSN 2377-3782

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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.

Item Type: Article
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Clustering; Computational Trust; Feature extraction; Knowledge acquisition; Model classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 22 May 2018 09:26
Last Modified: 04 Sep 2021 10:29
URI: https://researchonline.ljmu.ac.uk/id/eprint/8690
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