Exploring the Role of Machine Learning in Enhancing Access Control Systems: A Comprehensive Review

Abstract

Access control (AC) systems play a critical role in safeguarding sensitive data and resources. With the rise of complex and dynamic environments, human-driven solutions may be prone to errors, biases, and inconsistencies. Therefore, there is a growing need to enhance the accuracy, efficiency, and decision-making capabilities of AC systems. Machine learning (ML) techniques offer promising solutions to address these challenges and automate access control processes. This paper presents a systematic review of the application of ML techniques in AC systems. A comprehensive analysis is conducted, encompassing the identification and classification of ML models and their specific applications in AC. Through a systematic methodology, various factors such as AC models, ML techniques, empirical approaches, dataset characteristics, and evaluation metrics are considered. The review also evaluates the performance and effectiveness of the ML models in AC systems, highlighting their strengths and limitations. Additionally, the paper discusses the major challenges encountered in the field and identifies potential research directions for future investigations. This systematic review provides valuable insights into the current state of ML-based access control systems, facilitating further advancements and improvements in this important domain.