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Machine learning has been used to detect new malware in recent years, while malware authors have strong motivation to attack such algorithms.
Data mining methods for detection of new malicious executables
Matthew G Schultz, Eleazar Eskin, Erez Zadok, and Salvatore J Stolfo · 2001
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Learning to detect malicious executables in the wild
Jeremy Z Kolter and Marcus A Maloof · 2004
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Learning to detect and classify malicious executables in the wild
J Zico Kolter and Marcus A Maloof · 2006
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Microsoft portable executable and common object file format specification, 2013
Microsoft · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
Evaluation of defensive methods for dnns against multiple adversarial evasion models
Xinyun Chen, Bo Li, and Yevgeniy Vorobeychik · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2016
Later among the works it cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
Later among the works it cites.
Practical black-box attacks against deep learning systems using adversarial examples
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2016
Later among the works it cites.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Later among the works it cites.
Distillation as a defense to adversarial perturbations against deep neural networks
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Adversarial perturbations against deep neural networks for malware classification
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel · 2016
Cited alongside, same era.
A general retraining framework for scalable adversarial classification
Bo Li, Yevgeniy Vorobeychik, and Xinyun Chen · 2016
Cited alongside, same era.
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Later among the works it cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Later among the works it cites.
Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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