Keywords
intelligent control
Bayesian networks
cybersecurity
collaborative protection
How to Cite
Abstract
As network technology advances rapidly, the complexity of network security threats is growing, and it is difficult for traditional protection measures to cope with new attacks. This study explores the synergistic characteristics of reinforcement learning intelligent control and the Bayesian network model in network security protection. Reinforcement learning realizes dynamic responses to network attacks through self-learning and optimization strategies; Bayesian networks use probabilistic reasoning to accurately evaluate network status and potential threats by constructing a collaborative protection system integrating the two and conducting experimental verification in the simulated environment. The results show that compared with a single model, the collaborative system improves the threat detection accuracy by 25% and shortens the response time by 40%. Further analysis shows that reinforcement learning intelligent control effectively improves the adaptive ability of the system, while the Bayesian network model enhances the accuracy of threat prediction. The synergy between the two significantly improves the overall efficiency of network security protection. The study offers novel theories and methods to enhance network security and supports the development of intelligent security systems.
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