Enhancing IoT Security Through User Categorization and Aberrant Behavior Detection Using RBAC and Machine Learning

proliferation of Internet of Things (IoT) technology in recent years has revolutionized several industries, providing customers with reliable and efficient IoT services. However, as the IoT ecosystem grows, attention has switched away from straightforward user access to the crucial topic of security. Among others, there is a need to categorize users according to the actions they conduct as well as according to aberrant user behavior. By utilizing Role-Based Access Control (RBAC) and merging the categorization of access rights with the identification of aberrant behavior, access points to the Internet of Things will be strengthened in terms of security and dependability. A system is proposed to identify security flaws and prompt rapid remediation, with the incorporation of a classification of aberrant user behaviors which, in turn, offers a thorough defense against any outside threats. Three classification methods which are Support Vector Machine (SVM), Local Outlier Factor (LOF), and Isolation Forest (IF), were utilized in the study and their accuracy were compared. The results demonstrate the effectiveness of machine learning approaches using a dataset for IoT users, achieving high accuracy in identifying anomalous user behavior and enabling prompt implementation of necessary actions.