Along with computer technology's popularization and application and popularization, the network technology has been widely used, the resulting network security issues have become increasingly prominent, the network itself and network information system which is based on the potential security risks. Expounds the concepts of intrusion detection and data mining, common intrusion detection techniques and models, and analyzes the data mining technology in intrusion detection system application and optimization provide reference and perfect network intrusion detection system and reference.
Abstract. Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.
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