Survey of machine learning methods for detecting false data injection attacks in power systems

A Sayghe, Y Hu, I Zografopoulos, XR Liu… - IET Smart …, 2020 - Wiley Online Library
Over the last decade, the number of cyber attacks targeting power systems and causing
physical and economic damages has increased rapidly. Among them, false data injection …

Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence

A Chehri, I Fofana, X Yang - Sustainability, 2021 - mdpi.com
Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The
current security tools are almost perfect when it comes to identifying and preventing known …

Deep learning based attack detection for cyber-physical system cybersecurity: A survey

J Zhang, L Pan, QL Han, C Chen… - IEEE/CAA Journal of …, 2021 - ieeexplore.ieee.org
With the booming of cyber attacks and cyber criminals against cyber-physical systems
(CPSs), detecting these attacks remains challenging. It might be the worst of times, but it …

A hybrid deep learning model for discrimination of physical disturbance and cyber-attack detection in smart grid

K Bitirgen, ÜB Filik - International Journal of Critical Infrastructure Protection, 2023 - Elsevier
A smart grid (SG) consists of an interconnection of an electrical grid, communication, and
information networks. The rapid developments of SG technologies have resulted in complex …

Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning

M Ashrafuzzaman, S Das, Y Chakhchoukh, S Shiva… - Computers & …, 2020 - Elsevier
Stealthy false data injection attacks target state estimation in energy management systems
in smart power grids to adversely affect operations of the power transmission systems. This …

Electric power grid resilience to cyber adversaries: State of the art

T Nguyen, S Wang, M Alhazmi, M Nazemi… - IEEE …, 2020 - ieeexplore.ieee.org
The smart electricity grids have been evolving to a more complex cyber-physical ecosystem
of infrastructures with integrated communication networks, new carbon-free sources of …

A secured advanced management architecture in peer-to-peer energy trading for multi-microgrid in the stochastic environment

MA Mohamed, A Hajjiah, KA Alnowibet… - IEEE …, 2021 - ieeexplore.ieee.org
Careful consideration of grid developments illustrates the fundamental changes in its
structure which its developments have taken place gradually for a long time. One of the most …

Smart grid security and privacy: From conventional to machine learning issues (threats and countermeasures)

PH Mirzaee, M Shojafar, H Cruickshank… - IEEE access, 2022 - ieeexplore.ieee.org
Smart Grid (SG) is the revolutionised power network characterised by a bidirectional flow of
energy and information between customers and suppliers. The integration of power …

[HTML][HTML] Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions

JPA Yaacoub, HN Noura, O Salman - Internet of Things and Cyber-Physical …, 2023 - Elsevier
Abstract Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a
reputation for not only building Machine Learning (ML) models that rely on distributed …

False data injection threats in active distribution systems: A comprehensive survey

MA Husnoo, A Anwar, N Hosseinzadeh… - Future Generation …, 2023 - Elsevier
With the proliferation of smart devices and revolutions in communications, electrical
distribution systems are gradually shifting from passive, manually-operated and inflexible …