Auto-SGCR: Automated Generation of Smart Grid Cyber Range Using IEC 61850 Standard Models

2025-07-24

AI Summary

Smart grid cyber-range construction suffers from high costs, poor configurability, and limited reusability and sharing. Method: This paper proposes a model-driven automated range generation framework. It introduces SG-ML—a lightweight XML-based modeling language semantically aligned with IEC 61850—and supports human-in-the-loop modeling. An end-to-end toolchain automatically instantiates high-fidelity, executable ranges from standards-compliant models. Contribution/Results: Compared to proprietary systems, the framework significantly enhances configurability, portability, reproducibility, and open collaboration. Evaluated on a large-scale substation case study, it enables comprehensive testing of diverse attack vectors. The complete toolchain and example models are publicly released as open-source artifacts, bridging academic research and industrial practice.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Digitalization of power grids have made them increasingly susceptible to cyber-attacks in the past decade. Iterative cybersecurity testing is indispensable to counter emerging attack vectors and to ensure dependability of critical infrastructure. Furthermore, these can be used to evaluate cybersecurity configuration, effectiveness of the cybersecurity measures against various attack vectors, as well as to train smart grid cybersecurity experts defending the system. Enabling extensive experiments narrows the gap between academic research and production environment. A high-fidelity cyber range is vital as it is often infeasible to conduct such experiments and training using production environment. However, the design and implementation of cyber range requires extensive domain knowledge of physical and cyber aspect of the infrastructure. Furthermore, costs incurred for setup and maintenance of cyber range are significant. Moreover, most existing smart grid cyber ranges are designed as a one-off, proprietary system, and are limited in terms of configurability, accessibility, portability, and reproducibility. To address these challenges, an automated Smart grid Cyber Range generation framework is presented in this paper. Initially a human-/machine-friendly, XML-based modeling language called Smart Grid Modeling Language was defined, which incorporates IEC 61850 System Configuration Language files. Subsequently, a toolchain to parse SG-ML model files and automatically instantiate a functional smart grid cyber range was developed. The developed SG-ML models can be easily shared and/or modified to reproduce or customize for any cyber range. The application of Auto-SGCR is demonstrated through case studies with large-scale substation models. The toolchain along with example SG-ML models have been open-sourced.

Problem

• Automates smart grid cyber range creation for cybersecurity testing
• Reduces domain knowledge and cost barriers for cyber range setup
• Enhances configurability and reproducibility of smart grid simulations

Innovation

• Automated Smart Grid Cyber Range generation framework
• XML-based Smart Grid Modeling Language (SG-ML)
• Toolchain for parsing and instantiating cyber range