Graph neural networks in IoT: A survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
Quantitative analysis of power systems resilience: Standardization, categorizations, and challenges
Power systems incur considerable operational and infrastructural damages from high impact
low probability events such as natural disasters. It therefore becomes imperative to quantify …
low probability events such as natural disasters. It therefore becomes imperative to quantify …
Application of a dynamic line graph neural network for intrusion detection with semisupervised learning
G Duan, H Lv, H Wang, G Feng - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep learning (DL) greatly enhances binary anomaly detection capabilities through effective
statistical network characterization; nevertheless, the intrusion class differentiation …
statistical network characterization; nevertheless, the intrusion class differentiation …
Review of the data-driven methods for electricity fraud detection in smart metering systems
In smart grids, homes are equipped with smart meters (SMs) to monitor electricity
consumption and report fine-grained readings to electric utility companies for billing and …
consumption and report fine-grained readings to electric utility companies for billing and …
Joint detection and localization of stealth false data injection attacks in smart grids using graph neural networks
False data injection attacks (FDIA) are a main category of cyber-attacks threatening the
security of power systems. Contrary to the detection of these attacks, less attention has been …
security of power systems. Contrary to the detection of these attacks, less attention has been …
A temporal graph neural network for cyber attack detection and localization in smart grids
This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and
localization of false data injection and ramp attacks on the system state in smart grids …
localization of false data injection and ramp attacks on the system state in smart grids …
Physics-informed machine learning for data anomaly detection, classification, localization, and mitigation: A review, challenges, and path forward
Advancements in digital automation for smart grids have led to the installation of
measurement devices like phasor measurement units (PMUs), micro-PMUs (-PMUs), and …
measurement devices like phasor measurement units (PMUs), micro-PMUs (-PMUs), and …
Graph-based detection for false data injection attacks in power grid
X Li, Y Wang, Z Lu - Energy, 2023 - Elsevier
False data injection attack (FDIA) is the main network attack type threatening power system.
FDIA affect the accuracy of data by modifying the measured values of measuring equipment …
FDIA affect the accuracy of data by modifying the measured values of measuring equipment …
Generalized graph neural network-based detection of false data injection attacks in smart grids
False data injection attacks (FDIAs) pose a significant threat to smart power grids. Recent
efforts have focused on develo** machine learning (ML)-based defense strategies against …
efforts have focused on develo** machine learning (ML)-based defense strategies against …
The Role of Generative Artificial Intelligence in Internet of Electric Vehicles
With the advancements of GenAI models, their capabilities are expanding significantly
beyond content generation and the models are increasingly being used across diverse …
beyond content generation and the models are increasingly being used across diverse …