The transition to smart grids has increased the vulnerability of electrical power systems to advanced cyber threats. To safeguard these systems, comprehensive security measures-including preventive, detective, and reactive strategies-are necessary. As part of the critical infrastructure, securing these systems is a major research focus, particularly against cyberattacks. Many methods are developed to detect anomalies and intrusions and assess the damage potential of attacks. However, these methods require large amounts of data, which are often limited or private due to security concerns. We propose a co-simulation framework that employs an autonomous agent to execute modular cyberattacks within a configurable environment, enabling reproducible and adaptable data generation. The impact of virtual attacks is compared to those in a physical lab targeting real smart grids. We also investigate the use of large language models for automating attack generation, though current models on consumer hardware are unreliable. Our approach offers a flexible, versatile source for data generation, aiding in faster prototyping and reducing development resources and time.

翻译：智能电网的转型增加了电力系统面临高级网络威胁的脆弱性。为保护这些系统，需要采取包括预防性、检测性和响应性策略在内的综合安全措施。作为关键基础设施的一部分，保障这些系统的安全是重要的研究焦点，尤其是在应对网络攻击方面。目前已有许多方法被开发用于检测异常与入侵，并评估攻击的潜在损害。然而，这些方法需要大量数据，而由于安全考虑，这些数据往往受限或属于私有。我们提出一种协同仿真框架，该框架采用自主代理在可配置环境中执行模块化网络攻击，从而实现可复现且可适配的数据生成。虚拟攻击的影响与针对真实智能电网的物理实验室攻击进行了对比。我们还研究了利用大语言模型实现攻击生成自动化的可能性，尽管当前在消费级硬件上运行的模型可靠性不足。我们的方法为数据生成提供了一个灵活、多功能的来源，有助于加快原型设计速度，并减少开发资源与时间投入。