Michael Moore

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All published works

Action	Title	Year	Authors
+	Exploiting the Shape of CAN Data for In-Vehicle Intrusion Detection	2018	Zachariah Tyree Robert A. Bridges Frank L. Combs Michael Moore
+	Exploiting the Shape of CAN Data for In-Vehicle Intrusion Detection	2018	Zachariah Tyree Robert A. Bridges Frank L. Combs Michael Moore
+ PDF Chat	Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning	2018	Christopher J. Frederickson Michael Moore Glenn Dawson Robi Polikar
+	Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning.	2018	Christopher J. Frederickson Michael Moore Glenn Dawson Robi Polikar

Common Coauthors

Coauthor	Papers Together
Christopher J. Frederickson	2
Robert A. Bridges	2
Zachariah Tyree	2
Glenn Dawson	2
Robi Polikar	2
Frank L. Combs	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Early Methods for Detecting Adversarial Images	2017	Dan Hendrycks Kevin Gimpel	2
+ PDF Chat	Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks	2018	Wonsuk Choi Hyo Jin Jo Samuel Woo Ji Young Chun Jooyoung Park Dong Hoon Lee	2
+ PDF Chat	Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization	2017	Luis Muñoz-González Battista Biggio Ambra Demontis Andrea Paudice Vasin Wongrassamee Emil Lupu Fabio Roli	2
+ PDF Chat	Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks	2016	Nicolas Papernot Patrick McDaniel Xi Wu Somesh Jha Ananthram Swami	2
+	Towards Deep Learning Models Resistant to Adversarial Attacks.	2018	Aleksander Mądry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu	2
+	Isolation Forest	2008	Fei Tony Liu Kai Ming Ting Zhi‐Hua Zhou	2
+	Is feature selection secure against training data poisoning?	2018	Xiao Huang Battista Biggio Gavin Brown Giorgio Fumera Claudia Eckert Fabio Roli	2
+	Poisoning Attacks against Support Vector Machines	2012	Battista Biggio Blaine Nelson Pavel Laskov	2
+	Scikit-learn: Machine Learning in Python	2012	Fabián Pedregosa Gaël Varoquaux Alexandre Gramfort Vincent Michel Bertrand Thirion Olivier Grisel Mathieu Blondel Peter Prettenhofer Ron J. Weiss Vincent Dubourg	2
+	An introduction to diffusion maps	2008	De la Porte J Herbst Bm Willy Hereman Van der Walt Stéfan	2
+	Practical Black-Box Attacks against Machine Learning	2016	Nicolas Papernot Patrick McDaniel Ian Goodfellow Somesh Jha Z. Berkay Celik Ananthram Swami	1
+	Adversarial Machine Learning at Scale	2016	Alexey Kurakin Ian Goodfellow Samy Bengio	1
+ PDF Chat	Wild patterns: Ten years after the rise of adversarial machine learning	2018	Battista Biggio Fabio Roli	1
+	Detecting Adversarial Samples from Artifacts	2017	Reuben Feinman Ryan R. Curtin Saurabh Shintre Andrew B. Gardner	1
+	Towards Deep Learning Models Resistant to Adversarial Attacks	2017	Aleksander Mądry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu	1
+	Adversarial and Clean Data Are Not Twins	2017	Zhitao Gong Wenlu Wang Wei‐Shinn Ku	1
+	Explaining and Harnessing Adversarial Examples	2014	Ian Goodfellow Jonathon Shlens Christian Szegedy	1
+	Evasion Attacks against Machine Learning at Test Time	2013	Battista Biggio Igino Corona Davide Maiorca Blaine Nelson Nedim Šrndić Pavel Laskov Giorgio Giacinto Fabio Roli	1
+ PDF Chat	Is data clustering in adversarial settings secure?	2013	Battista Biggio Ignazio Pillai Samuel Rota Bulò Davide Ariu Marcello Pelillo Fabio Roli	1
+	Poisoning Attacks against Support Vector Machines	2012	Battista Biggio Blaine Nelson Pavel Laskov	1
+	Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples.	2016	Nicolas Papernot Patrick McDaniel Ian Goodfellow Somesh Jha Z. Berkay Celik Ananthram Swami	1
+	Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization	2016	Alexander G. Ororbia C. Lee Giles Daniel Kifer	1
+	Detecting Adversarial Samples from Artifacts.	2017	Reuben Feinman Ryan R. Curtin Saurabh Shintre Andrew B. Gardner	1
+	Adversarial and Clean Data Are Not Twins	2023	Zhitao Gong Wenlu Wang	1
+	Early Methods for Detecting Adversarial Images	2016	Dan Hendrycks Kevin Gimpel	1
+	Extending Defensive Distillation	2017	Nicolas Papernot Patrick McDaniel	1
+	Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning	2018	Battista Biggio Fabio Roli	1