ReviewSensors (Basel, Switzerland)2021
Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Detection of internet of things network attacks by hybrid deep learning (CNN-LSTM) algorithm to enhance security.Scientific reports · 2026Article
- Edge Driven Trust Aware Threat Detection for IoT Enabled Intelligent Transportation Systems.Sensors (Basel, Switzerland) · 2026Article
- DSDAN: A Dual-Stream Multimodal Log-Flow Fusion Framework for Network Configuration Auditing and Compliance-Risk Detection.PloS one · 2026Article
- IoT Authentication in Federated Learning: Methods, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2025Article
- A Study on IoT Device Authentication Using Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control.Sensors (Basel, Switzerland) · 2025Article
- Dataset for authentication and authorization using physical layer properties in indoor environment.Data in brief · 2024Article
- Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection-Current Research Trends.Sensors (Basel, Switzerland) · 2024Review
- Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies.Sensors (Basel, Switzerland) · 2023Review
- A Novel Hybrid Convolutional Neural Network- and Gated Recurrent Unit-Based Paradigm for IoT Network Traffic Attack Detection in Smart Cities.Sensors (Basel, Switzerland) · 2023Article
- An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems.Journal of cloud computing (Heidelberg, Germany) · 2023Article
- Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial.Sensors (Basel, Switzerland) · 2022Article
- Authentication Schemes for Healthcare Applications Using Wireless Medical Sensor Networks: A Survey.SN computer science · 2022Article
- Cybersecurity in Power Grids: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the ongoing efforts for widespread Internet of Things (IoT) adoption, one of the key factors hindering the wide acceptance of IoT is security. Securing IoT networks such as the electric power grid or water supply systems has emerged as a major national and global priority. To address the security issue of IoT, several studies are being carried out that involve the use of, but are not limited to, blockchain, artificial intelligence, and edge/fog computing. Authentication and authorization are crucial aspects of the CIA triad to protect the network from malicious parties. However, existing authorization and authentication schemes are not sufficient for handling security, due to the scale of the IoT networks and the resource-constrained nature of devices. In order to overcome challenges due to various constraints of IoT networks, there is a significant interest in using machine learning techniques to assist in the authentication and authorization process for IoT. In this paper, recent advances in authentication and authorization techniques for IoT networks are reviewed. Based on the review, we present a taxonomy of authentication and authorization schemes in IoT focusing on machine learning-based schemes. Using the presented taxonomy, a thorough analysis is provided of the authentication and authorization (AA) security threats and challenges for IoT. Furthermore, various criteria to achieve a high degree of AA resiliency in IoT implementations to enhance IoT security are evaluated. Lastly, a detailed discussion on open issues, challenges, and future research directions is presented for enabling secure communication among IoT nodes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.