This project aims to develop an AI-driven Intrusion Detection System (IDS) tailored for IoT networks. Given the increasing security risks in IoT environments, this system will use machine learning algorithms to detect abnormal network traffic patterns and potential cyber threats in real time. The project will involve collecting IoT network traffic data, feature extraction, model training using supervised and unsupervised learning techniques, and real-time monitoring. The system will be tested using datasets like CICIDS or real IoT environments to evaluate its efficiency in detecting cyberattacks such as DDoS, malware, and unauthorized access.
Supervisor
Muneer Khalaf
Committee
Hamzeh Najeeb - Qusai Saeed
Students
Raed Mefleh - Sara Ahmad