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Found 4 papers, 1 papers with code
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids
False data injection attacks (FDIAs) pose a significant security threat to power system state estimation.
Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks
In this work, we study the vulnerabilities of DL-based energy theft detection through adversarial attacks, including single-step attacks and iterative attacks.
SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection
Energy theft causes large economic losses to utility companies around the world.
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems
We study the potential vulnerabilities of ML applied in CPSs by proposing Constrained Adversarial Machine Learning (ConAML), which generates adversarial examples that satisfy the intrinsic constraints of the physical systems.
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