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Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that enable misleading classification.
Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 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 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples (2014)
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Practical evasion of a learning-based classifier: A case study
Nedim Šrndic and Pavel Laskov · 2014
Earlier work this paper cites.
Deep neural network based malware detection using two dimensional binary program features
Joshua Saxe and Konstantin Berlin · 2015
Earlier work this paper cites.
Deepsign: Deep learning for automatic malware signature generation and classification
Omid E David and Nathan S Netanyahu · 2015
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Earlier work this paper cites.
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.
”why should I trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Dl4md: A deep learning framework for intelligent malware detection
William Hardy, Lingwei Chen, Shifu Hou, Yanfang Ye, and Xin Li · 2016
Cited alongside, same era.
Droiddetector: android malware characterization and detection using deep learning
Zhenlong Yuan, Yongqiang Lu, and Yibo Xue · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Malware detection by eating a whole exe
Edward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, and Charles Nicholas · 2017
Later among the works it cites.
Deep android malware detection
Niall McLaughlin, Jesus Martinez del Rincon, BooJoong Kang, Suleiman Yerima, Paul Miller, Sakir Sezer, Yeganeh Safaei, Erik Trickel, Ziming Zhao, Adam Doupe, et al · 2017
Later among the works it cites.
Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
Later among the works it cites.
Adversarial malware binaries: Evading deep learning for malware detection in executables
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, and Fabio Roli · 2018
Later among the works it cites.
Adversarial examples on discrete sequences for beating whole-binary malware detection
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Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
Cited alongside, same era.
Yes, machine learning can be more secure! a case study on android malware detection
Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, and Fabio Roli
Cited in the paper.
Felix Kreuk, Assi Barak, Shir Aviv-Reuven, Moran Baruch, Benny Pinkas, and Joseph Keshet · 2018
Later among the works it cites.
Lemna: Explaining deep learning based security applications
Wenbo Guo, Dongliang Mu, Jun Xu, Purui Su, Gang Wang, and Xinyu Xing · 2018
Later among the works it cites.
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
H. S. Anderson and P. Roth · 2018
Later among the works it cites.