Title: Improving IoT access control with context-aware machine learning: reducing bias and enhancing accuracy

Authors: Surendra Tyagi; Yamuna Prasad; Devesh C. Jinwala; Subhasis Bhattacharjee

Addresses: Department of Computer Science and Engineering, Indian Institute of Technology, Jagti, Jammu, 181221, Jammu and Kashmir, India ' Department of Computer Science and Engineering, Indian Institute of Technology, Jagti, Jammu, 181221, Jammu and Kashmir, India ' Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Ichchhanath, Surat, 395007, Gujarat, India ' Department of Computer Science and Engineering, Indian Institute of Technology, Jagti, Jammu, 181221, Jammu and Kashmir, India

Abstract: With the vast amounts of data generated in typical IoT applications, there are challenges in collecting, modelling, reasoning, and distributing the context of the sensed data. The context of the sensor data can trigger an idea about using it in many applications where access can be regulated partially or entirely. Traditional access control methods provide coarse controls, viz., full or no access. Fine-grained access control can be developed using context awareness. Policy-based access control methods in dynamic environments require periodic updates to adapt policies based on data patterns. Machine learning can be used to an advantage when an access control method requires periodic updates of policies in dynamic environments, focusing on generating and adapting policies based on data patterns. This paper investigates applying context-aware machine learning (CAML) models, which reduces bias in learning while simultaneously building policies based on patterns inferred from trustworthy access reports available as past data. CAML improved accuracy to 99.9% for the smart home dataset in most cases.

Keywords: context-aware; machine learning; internet of things; IoT; access control.

DOI: 10.1504/IJAHUC.2025.146121

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.49 No.1, pp.60 - 73

Received: 09 Feb 2024
Accepted: 30 Oct 2024
Published online: 07 May 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article