Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks

16 October 2020

Papers citing "Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks"

8 / 8 papers shown

Title
A Novel Combined Data-Driven Approach for Electricity Theft Detection Kedi Zheng Qixin Chen Yijiao Wang Chongqing Kang Q. Xia 73 211 0 11 Nov 2024
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems Jiangnan Li Yingyuan Yang Jinyuan Stella Sun K. Tomsovic Jin Young Lee AAML 88 54 0 12 Mar 2020
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 61 950 0 14 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 472 3,147 0 04 Nov 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 266 8,579 0 16 Aug 2016
Adversarial Diversity and Hard Positive Generation Andras Rozsa Ethan M. Rudd Terrance E. Boult 87 257 0 05 May 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 151 4,903 0 14 Nov 2015
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 277 14,961 1 21 Dec 2013