no code implementations • 25 Jun 2022 • Osman Boyaci, M. Rasoul Narimani, Katherine Davis, Erchin Serpedin
This study employs Infinite Impulse Response (IIR) Graph Neural Networks (GNN) to efficiently model the inherent graph network structure of the smart grid data to address the cyberattack localization problem.
no code implementations • 5 Feb 2022 • Osman Boyaci, M. Rasoul Narimani, Katherine Davis, Erchin Serpedin
The interconnection between different components in a power system causes failures to easily propagate across the system.
no code implementations • 25 Dec 2021 • Osman Boyaci, Mohammad Rasoul Narimani, Katherine Davis, Erchin Serpedin
As a highly complex and integrated cyber-physical system, modern power grids are exposed to cyberattacks.
no code implementations • 24 Apr 2021 • Osman Boyaci, Mohammad Rasoul Narimani, Katherine Davis, Muhammad Ismail, Thomas J Overbye, Erchin Serpedin
To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems.
no code implementations • 5 Apr 2021 • Osman Boyaci, Amarachi Umunnakwe, Abhijeet Sahu, Mohammad Rasoul Narimani, Muhammad Ismail, Katherine Davis, Erchin Serpedin
False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids.