FGSM adversarial perturbations on remote measurements can fool ML-based FDIA detectors in differential relays up to 99.7% of the time; adversarial training lowers the fooling rate.
Anomaly-based detection of cyberattacks on line current differential relays,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Adversarial Attacks on Deep Learning-Based False Data Injection Detection in Differential Relays
FGSM adversarial perturbations on remote measurements can fool ML-based FDIA detectors in differential relays up to 99.7% of the time; adversarial training lowers the fooling rate.