Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids

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.

Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks

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.

Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks

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.

Spatio-Temporal Failure Propagation in Cyber-Physical Power Systems

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.

Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids

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.