The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.
Machine Learning Based Trust Computational Model for IoT Services
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“In 2019, Gao Z and others [47] designed a multidimensional adaptive trust evaluation mechanism using edge computing for nodes with weak computing power that widely exist in the network (especially local edge computing networks), which improved node trust.The robustness of the evaluation, but the flexibility of the model, is not high and should be improved in future research.”
From A Survey on Zero Trust Architecture: Challenges and Future Trends (2022) · cites this paper as [47]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Jolliffe, I. T. (2005) · Wiley eBooks · doi:10.1002/0470013192.bsa501 · Cited by 14,522
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
On the limited memory BFGS method for large scale optimization
Liu, D. C., & Nocedal, J. M. (1989) · Mathematical Programming · doi:10.1007/bf01589116 · Cited by 9,062
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,536
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
A Practical Guide to Support Vector Classication
Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,056
Information Theory, Inference, and Learning Algorithms
(2004) · Kybernetes · doi:10.1108/03684920410534506 · Cited by 4,937
Formalising Trust as a Computational Concept
Marsh, S. (1994) · Cited by 1,472
Atzori, L., Iera, A., Morabito, G., & Nitti, M. (2012) · Computer Networks · doi:10.1016/j.comnet.2012.07.010 · Cited by 1,238 · cited 2×
“However, in an IoT model, objects are always in relationship with their owner (Owner Object Relationship-OOR) and hence the static or dynamic nature of the OOR always affects the CLR [2].”
Research works printed but not linked: 6
- wang, 0, LogitTrust: A logit regression-based trust model for mobile ad hoc networks, Proc 6th ASE Int Conf Privacy Security Risk Trust, 1
- nitti, 0, A subjective model for trustworthiness evaluation in the social IoT, Proc IEEE Int Symp Personal Indoor Mobile Radio Commun, 18
- hu, 2014, SocialTrust: Enabling long-term social cooperation in p2p services, Springer P2P Netw Appl, 7, 525
- 2017, Overview of trust provisioning for information and communication technology infrastructures and services ITU-T Recommendation Y 3052
- wang, 0, Logittrust: A logit regression-based trust model for mobile ad hoc networks, Proc 6th ASE Int Conf Privacy Security Risk Trust, 1
- pietil, 0, MobiClique: Middleware for mobile social networking, Proc 2nd ACM Workshop Online Social Netw, 49
Cited by
Sorted by citations
A Survey on Metaverse: Fundamentals, Security, and Privacy
Wang, Y., Su, Z., Zhang, N., Xing, R., Liu, D., Luan, T. H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3202047 · Cited by 1,655
Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100227 · Cited by 500
A Survey on Zero Trust Architecture: Challenges and Future Trends
He, Y., Huang, D., Chen, L., Ni, Y., & Ma, X. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/6476274 · Cited by 348
“As early as 2018, Jayasinghe U and others [46] and others applied machine learning technology to the node trust evaluation framework, implemented a data credibility labeling method based on unsupervised learning technology, and based on this node trust labeling method.”
MethodWhen Machine Learning Meets Privacy in 6G: A Survey
Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211
Arisdakessian, S., Wahab, O. A., Mourad, A., Otrok, H., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3203249 · Cited by 227
Metadata
- Authors
- Jayasinghe, U., Lee, G. M., Um, T.-W., & Shi, Q.
- Affiliations
- Liverpool John Moores University (GB)
- Chosun University (KR)
- Venue
- IEEE Transactions on Sustainable Computing
- Published
- 2019-01-01
- Volume
- 4
- Issue
- 1
- Pages
- 39-52
- ISSN
- 2377-3782 (ISSN-L)
- 2377-3790
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
