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Beyond its highly publicized victories in Go, there have been numerous successful applications of deep learning in information retrieval, computer vision and speech recognition.
Dropout: a simple way to prevent neural networks from overfitting
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Evasion Attacks against Machine Learning at Test Time.. In ECML/PKDD (3)
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli. 2013 · 2013
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Large-Scale Malware Classification Using Random Projections and Neural Networks. In Proceedings IEEE Conference on Acoustics, Speech, and Signal Processing
George Dahl, Jack W. Stokes, Li Deng, and Dong Yu. 2013 · 2013
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
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Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio. 2014 · 2014
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The Flaw Lurking In Every Deep Neural Net
Mike James. 2014 · 2014
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Practical Evasion of a Learning-Based Classifier: A Case Study. In Proceedings of the 2014 IEEE Symposium on Security and Privacy
Nedim Srndic and Pavel Laskov. 2014 · 2014
Cited alongside, same era.
Intriguing properties of neural networks. In International Conference on Learning Representations
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014 · 2014
Cited alongside, same era.
Zhenlong Yuan, Yongqiang Lu, Zhaoguo Wang, and Yibo Xue. 2014 · 2014
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Deep Learning on Disassembly
Matt Wolff Andrew Davis. 2015 · 2015
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Malicious behavior detection using windows audit logs. In Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security
Konstantin Berlin, David Slater, and Joshua Saxe. 2015 · 2015
Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features
Joshua Saxe and Konstantin Berlin. 2015 · 2015
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DeepDGA: Adversarially-Tuned Domain Generation and Detection
Hyrum Anderson, Jonathan Woodbridge, and Bobby Filar. 2016 · 2016
Closest in time.
Deep Learning Neural Nets Are Effective Against AI Malware
BIZETY 2016 · 2016
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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 · 2016
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Machine-Learning Algorithm Combs the Darknet for Zero Day Exploits, and Finds Them
MIT Technology Review 2016 · 2016
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Cited alongside, same era.
Deep Learning can be easily fooled
Ran Bi. 2015 · 2015
Cited alongside, same era.
Deep Learning Adversarial Examples – Clarifying Misconceptions
Ian Goodfellow. 2015 · 2015
Cited alongside, same era.
Antivirus That Mimics the Brain Could Catch More Malware
Will Knight. 2015 · 2015
Cited alongside, same era.
Baidu, the Chinese Google, Is Teaching AI to Spot Malware
Cade Metz. 2015 · 2015
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. 2015 · 2015
Cited alongside, same era.
How to use deep learning AI to detect and prevent malware and APTs in real-time
Linda Musthaler. 2016 · 2016
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Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization
Alexander G. Ororbia II, C. Lee Giles, and Daniel Kifer. 2016 · 2016
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The limitations of deep learning in adversarial settings. In 2016 IEEE European Symposium on Security and Privacy (EuroS&P)
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami. 2016 · 2016
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Internet Security Threat Report
Symantec 2016 · 2016
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