Real-Time Threat Management Using Deep Q-Learning and Mininet

The research combines Deep Q-Learning(DQN) with a Mininet-based network simulation and Scapy intrusions detection system (IDS) for malicious traffic prioritizing. The RL agent continuously learns to act based on real-time traffic data and builds an adaptive model that detects and prioritizes threats such as SYN Flood, UDP Flood, Slowloris, HTTP Flood etc. The RL agent then adjusts its policy via the seamless combination of network simulation and live traffic detection to make sure lossy channels representing high-severity attacks are dealt with first, lessening the damage. The system improves the identification of intrusions by stimulating the intrusion prioritization dynamically with continuous learning and feedback, increasing network security.