Graph neural networks in IoT: A survey

G Dong, M Tang, Z Wang, J Gao, S Guo, L Cai… - ACM Transactions on …, 2023 - dl.acm.org
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 …

Quantitative analysis of power systems resilience: Standardization, categorizations, and challenges

A Umunnakwe, H Huang, K Oikonomou… - … and Sustainable Energy …, 2021 - Elsevier
Power systems incur considerable operational and infrastructural damages from high impact
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 …

Review of the data-driven methods for electricity fraud detection in smart metering systems

MM Badr, MI Ibrahem, HA Kholidy, MM Fouda, M Ismail - Energies, 2023 - mdpi.com
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 …

Joint detection and localization of stealth false data injection attacks in smart grids using graph neural networks

O Boyaci, MR Narimani, KR Davis… - … on Smart Grid, 2021 - ieeexplore.ieee.org
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 …

A temporal graph neural network for cyber attack detection and localization in smart grids

SH Haghshenas, MA Hasnat… - 2023 IEEE Power & …, 2023 - ieeexplore.ieee.org
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 …

Physics-informed machine learning for data anomaly detection, classification, localization, and mitigation: A review, challenges, and path forward

MJ Zideh, P Chatterjee, AK Srivastava - IEEE Access, 2023 - ieeexplore.ieee.org
Advancements in digital automation for smart grids have led to the installation of
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 …

Generalized graph neural network-based detection of false data injection attacks in smart grids

A Takiddin, R Atat, M Ismail, O Boyaci… - … on Emerging Topics …, 2023 - ieeexplore.ieee.org
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 …

The Role of Generative Artificial Intelligence in Internet of Electric Vehicles

H Zhang, D Niyato, W Zhang, C Zhao… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
With the advancements of GenAI models, their capabilities are expanding significantly
beyond content generation and the models are increasingly being used across diverse …