Information Security

AI-Driven Intrusion Detection System for IoT Networks Using Real-World Traffic Data This project focuses on designing an intelligent intrusion detection system (IDS) for IoT networks using artificial intelligence techniques. The system analyzes real-world network traffic data generated by IoT devices to automatically detect malicious activities and cyber-attacks. By applying machine learning algorithms, the proposed IDS learns normal and abnormal traffic patterns, enabling accurate and timely attack detection. The model aims to improve security by reducing false alarms and enhancing detection performance compared to traditional rule-based systems. This AI-driven approach supports scalable, adaptive, and reliable protection for modern IoT environments, where resource-constrained devices are highly vulnerable to evolving cyber threats.