Deep autoencoder-based anomaly detection of electricity theft cyberattacks in smart grids
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 …
(AMIs) is challenging due to the limited availability of electricity theft datasets (ie, malicious …
Robust electricity theft detection against data poisoning attacks in smart grids
Data-driven electricity theft detectors rely on customers' reported energy consumption
readings to detect malicious behavior. One common implicit assumption in such detectors is …
readings to detect malicious behavior. One common implicit assumption in such detectors is …
ETD-ConvLSTM: A deep learning approach for electricity theft detection in smart grids
X Xia, J Lin, Q Jia, X Wang, C Ma… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In smart grids, various Internet-of-Things-based (IoT-based) components are massively
deployed across the power systems. However, most of these IoT-based components have …
deployed across the power systems. However, most of these IoT-based components have …
A hybrid ConvLSTM-based anomaly detection approach for combating energy theft
In a conventional power grid, energy theft is difficult to detect due to limited communication
and data transition. The smart meter along with big data mining technology leads to …
and data transition. The smart meter along with big data mining technology leads to …
Deep learning detection of electricity theft cyber-attacks in renewable distributed generation
Unlike the existing research that focuses on detecting electricity theft cyber-attacks in the
consumption domain, this paper investigates electricity thefts at the distributed generation …
consumption domain, this paper investigates electricity thefts at the distributed generation …
Deep recurrent electricity theft detection in AMI networks with random tuning of hyper-parameters
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring
and billing purposes. However, such an approach suffers from electricity theft cyberattacks …
and billing purposes. However, such an approach suffers from electricity theft cyberattacks …
Robust data-driven detection of electricity theft adversarial evasion attacks in smart grids
Existing machine learning-based detectors of electricity theft cyberattacks are trained to
detect only simple traditional types of cyberattacks while neglecting complex ones like …
detect only simple traditional types of cyberattacks while neglecting complex ones like …
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 …
Detecting electricity theft cyber-attacks in AMI networks using deep vector embeddings
Despite being equipped with advanced metering infrastructure (AMI), utility companies are
subjected to electricity theft cyber-attacks. The existing machine learning-based detectors do …
subjected to electricity theft cyber-attacks. The existing machine learning-based detectors do …
Electricity theft detection in smart grids based on deep neural network
Electricity theft is a global problem that negatively affects both utility companies and
electricity users. It destabilizes the economic development of utility companies, causes …
electricity users. It destabilizes the economic development of utility companies, causes …