AgentCyTE：利用智能体人工智能生成网络安全培训与实验场景 (AgentCyTE: Leveraging Agentic AI to Generate Cybersecurity Training & Experimentation Scenarios)

Designing realistic and adaptive networked threat scenarios remains a core challenge in cybersecurity research and training, still requiring substantial manual effort. While large language models (LLMs) show promise for automated synthesis, unconstrained generation often yields configurations that fail validation or execution. We present AgentCyTE, a framework integrating LLM-based reasoning with deterministic, schema-constrained network emulation to generate and refine executable threat environments. Through an agentic feedback loop, AgentCyTE observes scenario outcomes, validates correctness, and iteratively enhances realism and consistency. This hybrid approach preserves LLM flexibility while enforcing structural validity, enabling scalable, data-driven experimentation and reliable scenario generation for threat modeling and adaptive cybersecurity training. Our framework can be accessed at: https://github.com/AnantaaKotal/AgentCyTE

翻译：设计真实且自适应的网络威胁场景仍然是网络安全研究与培训的核心挑战，目前仍需大量人工投入。尽管大型语言模型（LLMs）在自动化合成方面展现出潜力，但无约束的生成常导致配置无法通过验证或执行。我们提出了AgentCyTE框架，该框架将基于LLM的推理与确定性、模式约束的网络仿真相结合，以生成并优化可执行的威胁环境。通过智能体反馈循环，AgentCyTE观察场景结果、验证正确性，并迭代提升真实性与一致性。这种混合方法在保持LLM灵活性的同时确保了结构有效性，从而实现了可扩展的数据驱动实验，并为威胁建模和自适应网络安全培训提供了可靠的场景生成。我们的框架可通过以下链接访问：https://github.com/AnantaaKotal/AgentCyTE