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 …

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 …

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 …

Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …

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

In smart power grids, electricity theft causes huge economic losses to electrical utility
companies. Machine learning (ML), especially deep neural network (DNN) models hold …

Due to the diversity of power consumption patterns, the false positive rate (FPR) of data-
driven electricity theft detection (ETD) methods is too high to meet practical needs, which …

Despite being equipped with advanced metering infrastructure (AMI), utility companies are
subjected to electricity theft cyber-attacks. The existing machine learning-based detectors do …

Demand response (DR) systems are prone to false data injection attacks (FDIA), which
present substantial economic and operational hazards. Notwithstanding their significance …

Intrusion detection systems (IDSs) investigate various attacks, identify malicious patterns,
and implement effective control strategies. With the recent advances in machine learning …