Abstract

Internet of Things (IoT)-based environments collect and generate huge amounts of data about users, their activities, and their surroundings, which can disclose some sensitive information and threat their privacy. Hence, user data collected and handled by IoT-based applications need to be exploited and secured in an appropriate way to protect personal data and user privacy. Therefore, we aim at designing a user-centric approach for user privacy protection based on two main blocks, namely (i) a habit-based approach for anomaly-based intrusion detection system, and (ii) semantic-based firewall for access control and communication security. We detail in this paper the design of the former block by introducing a generic algorithm for user habit learning as a pillar of our anomaly detection system, which is then instantiated by an intuitionistic fuzzy sets model to illustrate how it operates in a real world use-case.