Construction and Simulation of an Automatic Generation Model of Security Defense Strategies Based on Reinforcement Learning
Sujie Yang, Xiaolong Deng, Jingying Wang, Ruizhi Xiong, Junwen Lu · Intelligent Computing · 2025
To address the issue of traditional defense strategies being inflexible in handling new types of attacks due to resource rigidification, this study focuses on constructing a self-generating defense strategy model through algorithm design. This model allows for flexible adjustments to dynamically adapt to the specific equipment’s network topology, using simulation design to construct a network attack and defense environment. This addresses the shortcomings of existing simulation designs that lack real-world scenario features and practical network attack analysis, thereby providing an experimental platform for deep research on defense strategies. Through theoretical analysis of deep reinforcement learning algorithms, a defense strategy generation scheme based on these algorithms is designed and optimized according to environmental characteristics. Finally, comparative experimental simulations are conducted by evaluating win rates and resource consumption. This study proposes a self-generating scheme of strategies based on deep reinforcement learning within the new network equipment architectures. Our scheme automatically generates defense strategies to counter emerging network attacks in the network, flexibly allocates network resources, executes dynamic proactive defense strategies, and automatically eliminates the impact of unknown attacks. In addition, it continuously expands the expert knowledge base and uses experimental data from parallel network environments to enhance the defensive capabilities of intelligent agents.