Context-aware security and machine learning for access control: A systematic mapping and taxonomies

This study explores the enhancement of user security in smart environments through the integration of Context-Aware Security (CAS) with Machine Learning (ML). A systematic mapping covering publications from 2013 to 2024 analyzed 75 primary studies that combine CAS, security, and ML, particularly in the context of access control and identity validation. The analysis revealed that Access Control (AC), managing information access based on contextual data, emerged as a predominant area of interest, with 30 studies specifically addressing AC within CAS and ML. The main contributions include taxonomies for context awareness, security mechanisms, ML techniques, and AC mechanisms. These taxonomies categorize and illustrate the current state of research in this interdisciplinary area. Key findings highlight several challenges, such as the difficulty of obtaining datasets due to the sensitive nature of security data, and the limited number of studies thoroughly exploring the integration of CAS, AC, and ML. Consequently, the potential benefits of ML in CAS, particularly for AC, remain underutilized, and traditional techniques continue to dominate the field. The study suggests that further research is needed to effectively integrate ML into CAS to modernize AC systems and improve adaptability to dynamic environments. Addressing these challenges could lead to more secure, responsive, and user-friendly systems in IoT and mobile environments.