Physics-aware AI-based Approach for Cyber Intrusion Detection in Substation Automation Systems

With the integration of information and communications technology and intelligent electric devices, substation automation systems (SAS) greatly boost the efficiency of power system monitoring and control. However, substations also bring new vulnerabilities at the frontier of the wide-area monitoring and control infrastructure of a bulk power system. They are known to be attractive targets for attackers. In this project, we will research, develop, and validate algorithms that defend against cyberattacks that aim to disrupt substation operations by maliciously changing measurements and/or spoofing spurious control commands.

We propose multiple use-inspired AI innovations that crucially leverage concurrent capabilities of SAS to transform the cyber security of power systems, including (i) a framework that synergizes optimization-based attack modelling with inverse reinforcement learning for multi-stage attack detection, (ii) a decision-focused distributed CPS modelling approach, and (iii) a mathematical program with equilibrium constraints framework of adversarial unlearning for spoofing detection.

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