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Found 11 papers, 1 papers with code
A Firewall Optimization for Threat-Resilient Micro-Segmentation in Power System Networks
These protocols are vulnerable to cyber threats that power systems, as cyber-physical critical infrastructure, must be protected against.
Bayesian Reinforcement Learning for Automatic Voltage Control under Cyber-Induced Uncertainty
Voltage control is crucial to large-scale power system reliable operation, as timely reactive power support can help prevent widespread outages.
Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids
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.
Spatio-Temporal Failure Propagation in Cyber-Physical Power Systems
The interconnection between different components in a power system causes failures to easily propagate across the system.
Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks
As a highly complex and integrated cyber-physical system, modern power grids are exposed to cyberattacks.
Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach
Using this model, a location-cum-domain based fusion framework is proposed and evaluated with different combination rules, that fuse multiple evidence from inter-domain and intra-domain sensors.
An Energy Management System Approach for Power System Cyber-Physical Resilience
Power systems are large scale cyber-physical critical infrastructure that form the basis of modern society.
An Ecological Robustness-Oriented Approach for Power System Network Expansion
Power systems are critical infrastructure for reliable and secure electric energy delivery.
Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks
To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems.
Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids
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.
Multi-Source Data Fusion for Cyberattack Detection in Power Systems
We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions.
