Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …

Potential malicious cyber-attacks to power systems which are connected to a wide range of
stakeholders from the top to tail will impose significant societal risks and challenges. The …

Designing an electricity theft cyberattack detector for the advanced metering infrastructures
(AMIs) is challenging due to the limited availability of electricity theft datasets (ie, malicious …

For accommodating rapidly increasing power demands, power systems are transitioning
from analog systems to systems with increasing digital control and communications …

Electricity theft has been a growing concern for the smart grid. It can be defined as follows:
illegal customers use energy from electric utilities without a contract or manipulate their …

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 …

The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

As one of the key components of smart grid, advanced metering infrastructure (AMI) provides
an immense number of data, making technologies such as data mining more suitable for …

Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered
significant attention in recent years. The application of artificial intelligence (AI), particularly …

Existing machine learning-based detectors of electricity theft cyberattacks are trained to
detect only simple traditional types of cyberattacks while neglecting complex ones like …