A review of machine learning approaches to power system security and stability
Increasing use of renewable energy sources, liberalized energy markets and most
importantly, the integrations of various monitoring, measuring and communication …
importantly, the integrations of various monitoring, measuring and communication …
A survey on industrial control system testbeds and datasets for security research
The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs)
open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore …
open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore …
Deep learning based attack detection for cyber-physical system cybersecurity: A survey
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 …
(CPSs), detecting these attacks remains challenging. It might be the worst of times, but it …
The role of machine learning in cybersecurity
Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …
systems, and many domains already leverage the capabilities of ML. However, deployment …
[HTML][HTML] An ensemble deep learning model for cyber threat hunting in industrial internet of things
By the emergence of the fourth industrial revolution, interconnected devices and sensors
generate large-scale, dynamic, and inharmonious data in Industrial Internet of Things (IIoT) …
generate large-scale, dynamic, and inharmonious data in Industrial Internet of Things (IIoT) …
Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids
Electricity theft is harmful to power grids. Integrating information flows with energy flows,
smart grids can help to solve the problem of electricity theft owning to the availability of …
smart grids can help to solve the problem of electricity theft owning to the availability of …
Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities
Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS).
However, due to the increasing complexity of CPSs and more sophisticated attacks …
However, due to the increasing complexity of CPSs and more sophisticated attacks …
An ensemble deep learning-based cyber-attack detection in industrial control system
A Al-Abassi, H Karimipour, A Dehghantanha… - Ieee …, 2020 - ieeexplore.ieee.org
The integration of communication networks and the Internet of Things (IoT) in Industrial
Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing …
Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing …
Deep learning based multi-channel intelligent attack detection for data security
Deep learning methods, eg, convolutional neural networks (CNNs) and Recurrent Neural
Networks (RNNs), have achieved great success in image processing and natural language …
Networks (RNNs), have achieved great success in image processing and natural language …
[HTML][HTML] Adversarial attacks on machine learning cybersecurity defences in industrial control systems
The proliferation and application of machine learning-based Intrusion Detection Systems
(IDS) have allowed for more flexibility and efficiency in the automated detection of cyber …
(IDS) have allowed for more flexibility and efficiency in the automated detection of cyber …