research
∙
01/29/2023
Mitigating Adversarial Effects of False Data Injection Attacks in Power Grid
Deep Neural Networks have proven to be highly accurate at a variety of t...
share
research
∙
02/17/2021
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids
False data injection attack (FDIA) is a critical security issue in power...
research
∙
10/16/2020
Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks
Effective detection of energy theft can prevent revenue losses of utilit...
research
∙
06/02/2020
SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection
Energy theft causes large economic losses to utility companies around th...
research
∙
03/12/2020
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems
Recent research demonstrated that the superficially well-trained machine...
research
∙
08/02/2018
A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data
Outsourcing data storage to remote cloud can be an economical solution t...
research
∙
08/02/2018
