A GAN-DQN enhanced AI-driven framework for real-time smart intrusion detection system in cooperative IoT networks

Key Points

• Real-time intrusion detection improves system security in IoT networks, enhancing responsiveness to threats.
• Using the GAN-DQN framework, the system demonstrates an accuracy of 95% in identifying potential security breaches.
• Assessment involves a novel algorithm combining GAN and DQN for effective learning from network behavior patterns.
• The framework's implementation in cooperative IoT settings highlights its potential for boosting network safety in real-time.