# Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/machine-learning-for-authentication-and-authorization-in-iot-taxonomy-challenges/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Kazi Istiaque Ahmed,Mohammad Tahir,Mohamed Hadi Habaebi,Sian Lun Lau,Abdul Ahad |
| Citations | 99 |
| DOI | 10.3390/s21155122 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/1424-8220/21/15/5122/pdf?version=1627749900 |
| OpenAlex ID | https://openalex.org/W3183608804 |
| PMID | 34372360 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Sian Lun Lau](https://scholariq.org/researchers/sian-lun-lau/)

## Paper journal

- [Sensors](https://scholariq.org/journals/sensors/)

## Paper primary topic

- [Blockchain Technology Applications and Security](https://scholariq.org/topics/blockchain-technology-applications-and-security/)

## Paper topics

- [Blockchain Technology Applications and Security](https://scholariq.org/topics/blockchain-technology-applications-and-security/)
- [User Authentication and Security Systems](https://scholariq.org/topics/user-authentication-and-security-systems/)
- [IoT and Edge/Fog Computing](https://scholariq.org/topics/iot-and-edge-fog-computing/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.