Abstract
Digital electrical substations are fundamental in providing a reliable basis for Smart Grids. However, the deployment of the IEC-61850 standard for communication between Intelligent Electronic Devices (IEDs) brings new security challenges. Intrusion Detection Systems (IDSs) play a vital role to ensure the proper function of digital substations services. However, the current literature lacks efficient IDS solutions for certain classes of attacks, such as the Masquerade attack. In this work, we propose the extraction and correlation of relevant multi-layer information to enable the deployment of machine learning-based IDSs in digital substations. Our results demonstrate that the proposed solution can detect attacks that are considered challenging in the literature, attaining an F1-score of up to 95.63% in the evaluated scenarios.
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