Network Security Defense Strategy of Deep Reinforcement Learning Oriented to Game Battle

Abstract

With the popularity of online games and the increasing frequency of game battles, game platforms are facing more and more network security threats and attacks. In order to effectively deal with these threats, this study proposes a network security defense strategy of Deep Reinforcement Learning (DRL) for game warfare. Through the modeling of game battle environment and the application of DRL algorithm, this strategy can monitor and identify all kinds of network attacks in game battle in real time, and take corresponding measures to defend and deal with them. In this study, the corresponding experimental environment is designed, and the network security defense strategy based on DRL is evaluated and analyzed. The experimental results show that the strategy has obvious advantages in network attack detection rate and false alarm rate, and has good stability and reliability. In addition, the strategy also shows strong generalization ability and adaptability, and can effectively deal with different types of network attack threats. The results of this study are of great significance for strengthening the network security defense of the game platform and improving the user experience and the stability of the game environment.