The advent of the Internet of Things (IoT) has revolutionized the concept of Smart Buildings by integrating various smart sensing and control devices to improve efficiency and user experience. This thesis explores the realm of Smart Buildings, with a focus on the challenges of access control. In particular, the research investigates the applicability and effectiveness of machine learning techniques to classify device activities within the Smart Building environment. As Smart Buildings continue to evolve, the need to ensure robust security measures becomes paramount. Traditional, intrinsically static access control methods often struggle to adapt to dynamic environments. This study explores the feasibility of using machine learning algorithms to dynamically classify device activities, with the aim of improving access control mechanisms.