Simulation of multi-stage attack and defense mechanisms in smart grids

The power grid is a vital infrastructure in modern society, essential for ensuring public safety and welfare. As it increasingly relies on digital technologies for its operation, it becomes more vulnerable to sophisticated cyber threats. These threats, if successful, could disrupt the grid's functionality, leading to severe consequences. To mitigate these risks, it is crucial to develop effective protective measures, such as intrusion detection systems and decision support systems, that can detect and respond to cyber attacks. Machine learning methods have shown great promise in this area, but their effectiveness is often limited by the scarcity of high-quality data, primarily due to confidentiality and access issues. In response to this challenge, our work introduces an advanced simulation environment that replicates the power grid's infrastructure and communication behavior. This environment enables the simulation of complex, multi-stage cyber attacks and defensive mechanisms, using attack trees to map the attacker's steps and a game-theoretic approach to model the defender's response strategies. The primary goal of this simulation framework is to generate a diverse range of realistic attack data that can be used to train machine learning algorithms for detecting and mitigating cyber attacks. Additionally, the environment supports the evaluation of new security technologies, including advanced decision support systems, by providing a controlled and flexible testing platform. Our simulation environment is designed to be modular and scalable, supporting the integration of new use cases and attack scenarios without relying heavily on external components. It enables the entire process of scenario generation, data modeling, data point mapping, and power flow simulation, along with the depiction of communication traffic, in a coherent process chain. This ensures that all relevant data needed for cyber security investigations, including the interactions between attacker and defender, are captured under consistent conditions and constraints. The simulation environment also includes a detailed modeling of communication protocols and grid operation management, providing insights into how attacks propagate through the network. The generated data are validated through laboratory tests, ensuring that the simulation reflects real-world conditions. These datasets are used to train machine learning models for intrusion detection and evaluate their performance, specifically focusing on how well they can detect complex attack patterns in power grid operations.