UNSW-MG24

One of the major challenges of microgrid systems is the lack of comprehensive Intrusion Detection System (IDS) datasets specifically for realistic microgrid systems' communication. To address the unavailability of comprehensive IDS datasets for realistic microgrid systems, this paper presents a UNSW-MG24 dataset based on realistic microgrid testbeds. This dataset contains synthesized benign network traffic from different campus departments, network flow of attack activities, system call traces, and microgrid-specific data from an integrated Festo LabVolt microgrid system. Additionally, pivoting attacks and mimicry attacks are implemented to increase this dataset's heterogeneity for intrusion detection. Comprehensive features such as network flow attributes, system call parameters, and power measurement metrics are extracted from the generated dataset. Finally, a comprehensive evaluation of the UNSW-MG24 dataset using Machine Learning (ML) intrusion detection algorithms demonstrates its ability to validate new AI-based cybersecurity strategies for microgrid systems.