An assessment of deep cyber-physical situational awareness of power system using real-time testbed

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In essence, smart grids are electrical networks that transmit and distribute electricity in a reliable, effective manner using information and communication technology (ICT). Trust and security are of the utmost importance. False data injection attacks (FDIA) are one of the newest security problems, and they can drastically impact the use of energy. By comprehending the correlation between the cyber layer and the physical layer, this research develops an effective and real-time technique to identify FDIA attacks in smart grids. We expose the existing vulnerabilities associated with existing detection techniques and analyze the performance of the proposed solution to detect and defend FDIA attacks against real-time measurements from the meters. We expose the implementation of the attack and the success of the detection algorithm using IEEE test systems and expand the real-world constraints associated with it. We show that the suggested method offers an accurate and dependable solution using realistic simulations based on the smart grid.

Embargo status: Restricted until 01/2025. To request the author grant access, click on the PDF link to the left.

cyber-physical, cyber-attacks, false date injection, machine learning, smart grid