Adaptive context-aware access control for IoT environments leveraging fog computing

Abstract

The increasing use of the Internet of Things (IoT) has driven the demand for enhanced and robust access control methods to protect resources from unauthorized access. A cloud-based access control approach brings significant challenges in terms of communication overhead, high latency, and complete reliance. In this paper, we propose a Fog-Based Adaptive Context-Aware Access Control (FB-ACAAC) framework for IoT devices, dynamically adjusting access policies based on contextual information to prevent unauthorised resource access. The main purpose of FB-ACAAC is to provide adaptability to changing access behaviors and context by bringing decision-making and information about policies closer to the end nodes of the network. FB-ACAAC improves the availability of resources and reduces the amount of time for information to be processed. FB-ACAAC extends the widely used eXtensible Access Control Markup Language (XACML) to manage access control decisions. Traditional XACML-based methods do not take into account changing environments, different contexts, and changing access behaviors and are vulnerable to certain types of attacks. To address these issues, FB-ACAAC proposes an adaptive context-aware XACML scheme for heterogeneous distributed IoT environments using fog computing and is designed to be context-aware, adaptable, and secure in the face of unauthorised access. The effectiveness of this new scheme is verified through experiments, and it has a low processing time overhead while providing extra features and improved security.