Research on Network Intrusion Detection System Based on Independent Q-learning

Aiming at the problem that traditional network intrusion detection systems have low accuracy in identifying complex and changeable network attack patterns, a multi-agent network intrusion detection system based on independent Q-learning is proposed. The system adopts a multi-agent architecture, and each agent uses an independent Q learning algorithm to monitor and assess network behavior, and improves the efficiency and accuracy of network intrusion detection through intelligent methods. The experimental results show that compared with traditional intrusion detection methods, the detection accuracy, response speed and robustness of this system are significantly improved. It can show good adaptability and generalization ability. It provides a new solution for the field of network security and has certain theoretical and practical value for building a more intelligent and reliable network security protection system.