Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems
Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …
been remarkable. Deep learning, in particular, has been extensively used to drive …
Analysis of cyber security attacks and its solutions for the smart grid using machine learning and blockchain methods
Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid
has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the …
has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the …
[HTML][HTML] Deep learning for power quality
This paper aims to introduce deep learning to the power quality community by reviewing the
latest applications and discussing the open challenges of this technology. Publications …
latest applications and discussing the open challenges of this technology. Publications …
LESSON: Multi-label adversarial false data injection attack for deep learning locational detection
Deep learning methods can not only detect false data injection attacks (FDIA) but also locate
attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep …
attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep …
Adversarial attack mitigation strategy for machine learning-based network attack detection model in power system
R Huang, Y Li - IEEE Transactions on Smart Grid, 2022 - ieeexplore.ieee.org
The network attack detection model based on machine learning (ML) has received extensive
attention and research in PMU measurement data protection of power systems. However …
attention and research in PMU measurement data protection of power systems. However …
Exploring targeted and stealthy false data injection attacks via adversarial machine learning
J Tian, B Wang, J Li, Z Wang, B Ma… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
State estimation methods used in cyber–physical systems (CPSs), such as smart grid, are
vulnerable to false data injection attacks (FDIAs). Although substantial deep learning …
vulnerable to false data injection attacks (FDIAs). Although substantial deep learning …
Datadriven false data injection attacks against cyber-physical power systems
Power systems are accelerating towards the transition to cyber-physical power systems
(CPPS). Such CPPS include myriads of sensors that generate huge amounts of data. The …
(CPPS). Such CPPS include myriads of sensors that generate huge amounts of data. The …
Countering evasion attacks for smart grid reinforcement learning-based detectors
Fraudulent customers in smart power grids employ cyber-attacks by manipulating their smart
meters and reporting false consumption readings to reduce their bills. To combat these …
meters and reporting false consumption readings to reduce their bills. To combat these …
Adversarial attack and training for deep neural network based power quality disturbance classification
L Zhang, C Jiang, Z Chai, Y He - Engineering Applications of Artificial …, 2024 - Elsevier
Power quality disturbance (PQD) can significantly affect the normal operation of the power
system. Deep neural network (DNN) can classify PQD with extremely high accuracy …
system. Deep neural network (DNN) can classify PQD with extremely high accuracy …
Vulnerability of machine learning approaches applied in iot-based smart grid: A review
Machine learning (ML) sees an increasing prevalence of being used in the Internet of Things
(IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be …
(IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be …