Zero Trust in the Context of IoT: Industrial Literature Review, Trends, and Challenges
- URL: http://arxiv.org/abs/2604.06272v1
- Date: Tue, 07 Apr 2026 07:02:17 GMT
- Title: Zero Trust in the Context of IoT: Industrial Literature Review, Trends, and Challenges
- Abstract summary: The Zero-trust (ZT) model is an increasingly popular model that relies on the idea that no trust should be granted to any entity (network, persons, devices) by default.<n>ZT model is gaining attention from both research and practice, with various levels of adequation between research developed and real-life applications.<n>An example of such an unspecified field is the integration of IoT/Smart Peripheral Devices (SPD).<n>Various reasons explain this gap: specificities of such resources (possibly lower energy/computation power), their lifecycle, and their use, strongly depending on the use of the whole platform
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Zero-trust (ZT) model is an increasingly popular model that relies on the idea that no trust should be granted to any entity (network, persons, devices) by default. ZT model is gaining attention from both research and practice, with various levels of adequation between research developed and real-life applications. NIST provided a standard to fulfill requirements of ZT architecture of network core but many practical aspects remain unspecified, some of them requiring solving first research challenges in order to be implemented efficiently. An example of such an unspecified field is the integration of IoT/Smart Peripheral Devices (SPD). Various reasons explain this gap: specificities of such resources (possibly lower energy/computation power), their lifecycle, and their use, strongly depending on the use of the whole platform IoT devices are part of. Moreover, additional difficulty to have a good understanding is induced by the fact that both Zero Trust and IoT are identified as promising trends in cybersecurity: many vendors/researchers tag their solutions as IoT integration into the ZT model, with little to no effective compliance to ZT model or standard. Industry is providing many practice-oriented literature, that has to be compared to academic work and standards, in order to consolidate the current state of knowledge and solutions offered to realize this integration. In this paper, we conduct a literature review of non-academic publications, in order to consolidate current knowledge, trends, and future challenges for the industrial integration of IoT devices in ZT architecture.
Related papers
- A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
arXiv Detail & Related papers (2025-08-26T08:38:01Z) - Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
arXiv Detail & Related papers (2025-05-26T06:44:31Z) - Large Language Models in the IoT Ecosystem -- A Survey on Security Challenges and Applications [1.1312948048543685]
The Internet of Things (IoT) and Large Language Models (LLMs) have been two major emerging players in the information technology era.<n>This literature survey explores the current state-of-the-art in applying LLMs within IoT.<n>It emphasizes their applications in various domains/sectors of society, the significant role they play in enhancing IoT security.
arXiv Detail & Related papers (2025-05-23T07:46:27Z) - Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0 [47.60424943340677]
The concept of digital twin (DT) is expected to revolutionize future industries and will lie at the heart of the vision of a future smart society, namely, Society 5.0.
This paper envisions a novel concept of an Internet of Federated Digital Twins (IoFDT) that holistically integrates DTs representing different Society 5.0 services.
arXiv Detail & Related papers (2023-12-11T14:56:27Z) - MultiIoT: Benchmarking Machine Learning for the Internet of Things [70.74131118309967]
The next generation of machine learning systems must be adept at perceiving and interacting with the physical world.
sensory data from motion, thermal, geolocation, depth, wireless signals, video, and audio are increasingly used to model the states of physical environments.
Existing efforts are often specialized to a single sensory modality or prediction task.
This paper proposes MultiIoT, the most expansive and unified IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 real-world tasks.
arXiv Detail & Related papers (2023-11-10T18:13:08Z) - A Novel IoT Trust Model Leveraging Fully Distributed Behavioral
Fingerprinting and Secure Delegation [3.10770247120758]
Internet of Things (IoT) solutions are experimenting a booming demand to make data collection and processing easier.
The higher the number of new capabilities and services provided in an autonomous way, the wider the attack surface that exposes users to data hacking and lost.
In this paper, we try to provide a contribution in this setting, tackling the non-trivial issues of equipping smart things with a strategy to evaluate, also through their neighbors, the trustworthiness of an object in the network before interacting with it.
arXiv Detail & Related papers (2023-10-02T07:45:49Z) - Fostering new Vertical and Horizontal IoT Applications with Intelligence
Everywhere [8.208838459484676]
Intelligence Everywhere is predicated on the seamless integration of IoT networks transporting a vast amount of data streams.
This paper discusses the state-of-the-art research and the principles of the Intelligence Everywhere framework.
It also introduces a novel perspective for the development of horizontal IoT applications.
arXiv Detail & Related papers (2023-09-30T11:59:39Z) - Towards Artificial General Intelligence (AGI) in the Internet of Things
(IoT): Opportunities and Challenges [55.82853124625841]
Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities.
This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things.
The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
arXiv Detail & Related papers (2023-09-14T05:43:36Z) - IoT trust and reputation: a survey and taxonomy [2.8998797177279076]
IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030.
To maximize the capability of these connected entities, trust and reputation among IoT entities is essential.
We propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed.
arXiv Detail & Related papers (2023-03-23T16:29:41Z) - The Internet of Federated Things (IoFT): A Vision for the Future and
In-depth Survey of Data-driven Approaches for Federated Learning [12.754922966044687]
The Internet of Things (IoT) is on the verge of a major paradigm shift.
In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge.
This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision.
arXiv Detail & Related papers (2021-11-09T18:52:26Z) - Computational Intelligence and Deep Learning for Next-Generation
Edge-Enabled Industrial IoT [51.68933585002123]
We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks.
In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework.
In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
arXiv Detail & Related papers (2021-10-28T08:14:57Z) - Federated Learning for Industrial Internet of Things in Future
Industries [106.13524161081355]
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems.
Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications.
Federated Learning (FL) is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge.
arXiv Detail & Related papers (2021-05-31T01:02:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.