Automatic Device Selection and Access Policy Generation Based on User Preference for IoT Activity Workflow
Open accessAbstract
The emerging Internet of Things (IoT) leads to a dramatic increase in type, quantity, and the number of functions that it can perform in a smart environment. Future smart environments will be even more abundant in terms of the number of devices and functionality that they can provide. This poses two main challenges for end users when automating their activities; a) they have to search through a vast number of IoT devices to identify the suitable devices that satisfy their preferences; and b) it is extremely difficult for users to define fine-grained security policies to support activities workflows. This paper introduces a new intelligent approach to overcome these challenges by a) enabling users to describe their required functionalities in the form of activity workflow; b) automatically selecting a group of devices to satisfy users functional requirements and maximise their preferences over device usage; c) systematically generating local network access control policies to ensure enforce the principle of least privilege. We study different heuristic search algorithms to find the preferred devices for a given workflow. Experiments results show that the Genetic Algorithm is the best, among the algorithms that we test, as it offers a balance between efficiency and effectiveness.
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Instead of users manually selecting devices, the authors in [164] have proposed an automatic scheme to abstract the workflows and automatically select suitable IoT devices required to achieve the policy management task, which can also facilitate the specification of accurate access policies in M2M communications as well as help enforce least privilege access.
“Instead of users manually selecting devices, the authors in [164] have proposed an automatic scheme to abstract the workflows and automatically select suitable IoT devices required to achieve the policy management task, which can also facilitate the specification of accurate access policies in M2M communications as well as help enforce least privilege access.”
From Zero Trust Architecture (ZTA): A Comprehensive Survey (2022) · cites this paper as [164]
Groups of IoT devices might work collectively for a specific workflow, which increases the complexity of manually implementing fine-grained access controls for M2M communications.
“In addition, groups of IoT devices might work collectively for a specific workflow, which increases the complexity of manually implementing fine-grained access controls for M2M communications [164].”
From Zero Trust Architecture (ZTA): A Comprehensive Survey (2022) · cites this paper as [164]
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References 36 · Cited by 12
Research works printed but not linked: 5
- [11]11. G. A. M. B. J. Corser, George Fink, "Internet of things security best practices," IEEE Internet Technology Policy Community White Paper, 2017.
- [27]27. M. Blackstock and R. Lea, "Fred: a hosted data flow platform for the iot built using node-red," Proceedings of MoTA, 2016.
- [34]34. A. L. M. Neto, A. L. Souza, I. Cunha, M. Nogueira, I. O. Nunes, L. Cotta, N. Gentille, A. A. Loureiro, D. F. Aranha, H. K. Patil et al., "Aot: Authentication and access control for the entire iot device life- cycle," in Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM. ACM, 2016, pp. 1-15.
- [37]37. C.-J. M. Liang, B. F. Karlsson, N. D. Lane, F. Zhao, J. Zhang, Z. Pan, Z. Li, and Y. Yu, "Sift: building an internet of safe things," in Proceedings of the 14th International Conference on Information Processing in Sensor Networks. ACM, 2015, pp. 298-309.
- [39]39. R. Daly, Q. Shen, and S. Aitken, "Learning bayesian networks: ap- proaches and issues," The knowledge engineering review, vol. 26, no. 2, pp. 99-157, 2011.
- Authors
- Al-Shaboti, M., Chen, A., & Welch, I.
- Affiliations
- Victoria University of Wellington (NZ)
- ORCID
- Welch, Ian
- Venue
- 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
- Publisher
- IEEE
- Published
- 2019-08
- Pages
- 769-774
- OpenAlex
- W2937918641
- Cited by
- 12
- References
- 36
- Citations by year
- 2026: 1 · 2024: 1 · 2022: 1 · 2021: 6 · 2020: 2 · 2019: 1
- FWCI
- 2.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Internet of Things, device selection, user preferences, smart environments, principle of least privilege, local network security, heuristic search, genetic algorithm, workflow automation
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