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Connected and digital electricity substations based on IEC–61850 standards enable novel applications. On the other hand, such connectivity also creates an extended attack surface. Therefore, Intrusion Detection Systems (IDSs) have become an essential component of safeguarding substations from malicious activities. However, in contrast to traditional information technology systems, there is a serious lack of realistic data for training, testing, and evaluating IDSs in smart grid scenarios. Many existing substation IDSs rely on datasets from other contexts or on proprietary datasets that do not allow reproducibility, validation, or performance comparison with competing algorithms. To address this issue, we propose the Efficacious Reproducer Engine for Network Operations (ERENO) synthetic traffic generation framework based on the IEC–61850 standard specifications. As an additional contribution, and as a proof-of-concept, we create and make available a suite of realistic IEC–61850 datasets that model 8 use cases, namely traffic for 7 common attacks and one for normal network traffic. Based on those datasets, we further evaluate how enriched features combining raw data from the substation can significantly improve intrusion detection performance. Our results suggest that it can improve F1-Score up to 47.22% for masquerade attacks.
Quincozes et al. (Mon,) studied this question.