SG-ML: Smart Grid Cyber Range Modelling Language

2025-09-11

AI Summary

Existing smart grid cyber ranges lack a unified, machine-readable, and human-understandable modeling methodology. Method: This paper proposes SG-ML—a domain-specific modeling language integrating representations of both power systems and cyber systems. SG-ML is the first to jointly extend IEC 61850 (SCL) and IEC 61131 (PLCopen XML), incorporating cybersecurity exercise primitives such as attack injection and scenario metadata—addressing critical gaps in industrial standards for range modeling. Formally defined via XML Schema, SG-ML is supported by a dedicated processor enabling automated model parsing and cyber range deployment. Contribution/Results: Experiments demonstrate that SG-ML significantly reduces modeling effort, enables user-defined physical and network topologies, and facilitates reproducible, scalable, and high-fidelity cyber range generation—supporting security research, training, and evaluation.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This work provides a detailed specification of the Smart Grid Modelling Language (SG-ML), which is designed for the automated generation of smart grid cyber ranges. SG-ML is defined as a set of XML schemas that describe a smart grid's configuration in both machine-readable and human-friendly ways, thereby bridging the gap between system modelling and automated deployment. Unlike prior ad-hoc approaches to cyber range design, SG-ML provides a unified methodology that integrates both power system and cyber network representations. The SG-ML model can be customized by users to meet specific requirements, such as emulating physical or cyber topologies and configuring network devices. An SG-ML Processor then parses this configured model to instantiate the cyber range environment. The modelling language leverages established standards like the IEC 61850 Substation Configuration Language (SCL) and IEC 61131 PLCopen XML to define power system topology, cyber network topology, and device configurations. This approach allows for the reuse of existing assets, reducing the effort needed to create the SG-ML model. To address gaps not covered by these standards such as attack injection parameters, scenario-specific metadata, and additional network constraints, SG-ML introduces proprietary schemas that complement standard models. Overall, SG-ML enables reproducible, scalable, and automated generation of realistic smart grid cyber ranges for research, training, and security assessment.

Problem

• Automated generation of smart grid cyber ranges
• Unified methodology integrating power and cyber systems
• Customizable modeling for reproducible and scalable environments

Innovation

• XML schemas for smart grid configuration
• Unified methodology integrating power and cyber
• Leverages IEC standards with proprietary extensions