Intelligent Cybersecurity Defense Strategies Based on Large Language Model Agents

As the complexity of cyberattacks continues to increase, traditional static defense strategies have shown significant limitations. This study proposes an intelligent cybersecurity defense framework based on large language model Agents, and conducts a comparative analysis of the effectiveness of various leading models in the cybersecurity defense domain. By developing an attack behavior model, designing an Agent decision-making mechanism, and constructing a defense strategy execution and evaluation system, we assess the defense performance of models such as GPT-4.5, Claude 3.7, LLaMA 3.3, and Gemini 2.0 under different attack scenarios. The experimental results demonstrate that defense strategies based on LLM Agents outperform traditional methods in terms of attack detection rate, response time, and false alarm rate. Furthermore, each model exhibits unique advantages in specific security contexts. This research provides new insights for the development of more adaptive and efficient cybersecurity defense systems.