Adversarial Attacks on Machine Learning Cybersecurity Defences in
Industrial Control Systems

Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems

10 April 2020

Papers citing "Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems"

10 / 10 papers shown

Title
Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems Alessandro Erba Riccardo Taormina S. Galelli Marcello Pogliani Michele Carminati S. Zanero Nils Ole Tippenhauer AAML 45 22 0 17 Jul 2019
Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set S. D. Antón Suneetha Kanoor Daniel Fraunholz Hans D. Schotten 90 80 0 28 May 2019
Gray-box Adversarial Testing for Control Systems with Machine Learning Component Shakiba Yaghoubi Georgios Fainekos AAML 27 65 0 31 Dec 2018
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks Moshe Kravchik A. Shabtai 44 274 0 21 Jun 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 161 3,171 0 01 Feb 2018
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks Thilo Strauss Markus Hanselmann Andrej Junginger Holger Ulmer AAML 45 134 0 11 Sep 2017
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN Weiwei Hu Ying Tan GAN 60 461 0 20 Feb 2017
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 81 1,735 0 24 May 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 66 3,947 0 24 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 176 18,922 0 20 Dec 2014