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Machine-learning methods have already been exploited as useful tools for detecting malicious executable files.
Peering inside the pe: A tour of the win32 portable executable file format, 1994
M. Pietrek · 1994
Earlier work this paper cites.
Data mining methods for detection of new malicious executables
M. G. Schultz, E. Eskin, E. Zadok, and S. J. Stolfo · 2001
Earlier work this paper cites.
N-gram-based detection of new malicious code
T. Abou-Assaleh, N. Cercone, V. Keselj, and R. Sweidan · 2004
Earlier work this paper cites.
Learning and classification of malware behavior
K. Rieck, T. Holz, C. Willems, P. Düssel, and P. Laskov · 2008
Earlier work this paper cites.
Prudent practices for designing malware experiments: Status quo and outlook
C. Rossow, C. J. Dietrich, C. Grier, C. Kreibich, V. Paxson, N. Pohlmann, H. Bos, and M. Van Steen · 2012
Earlier work this paper cites.
Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli · 2013
Earlier work this paper cites.
Exploring discriminatory features for automated malware classification
G. Yan, N. Brown, and D. Kong · 2013
Earlier work this paper cites.
Security evaluation of pattern classifiers under attack
B. Biggio, G. Fumera, and F. Roli · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Earlier work this paper cites.
Reducing Overfitting in Deep Networks by Decorrelating Representations
M. Cogswell, F. Ahmed, R. Girshick, L. Zitnick, and D. Batra · 2015
Cited alongside, same era.
Language Modeling with Gated Convolutional Networks
Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier · 2016
Cited alongside, same era.
The mythos of model interpretability
Z. C. Lipton · 2016
Cited alongside, same era.
Automatically evading classifiers
W. Xu, Y. Qi, and D. Evans · 2016
Cited alongside, same era.
Adversarial feature selection against evasion attacks
F. Zhang, P. Chan, B. Biggio, D. Yeung, and F. Roli · 2016
Cited alongside, same era.
Machine learning for malware detection, 2017
Kaspersky · 2017
Later among the works it cites.
Adversarial detection of flash malware: Limitations and open issues
D. Maiorca, B. Biggio, M. E. Chiappe, and G. Giacinto · 2017
Later among the works it cites.
Malware detection by eating a whole exe
E. Raff, J. Barker, J. Sylvester, R. Brandon, B. Catanzaro, and C. Nicholas · 2017
Later among the works it cites.
Internet security threat report, 2017
Symantec · 2017
Later among the works it cites.
Hardening classifiers against evasion: the good, the bad, and the ugly
L. Tong, B. Li, C. Hajaj, C. Xiao, and Y. Vorobeychik · 2017
Later among the works it cites.
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H. S. Anderson, A. Kharkar, B. Filar, and P. Roth · 2017
Cited alongside, same era.
Securedroid: Enhancing security of machine learning-based detection against adversarial android malware attacks
L. Chen, S. Hou, and Y. Ye · 2017
Cited alongside, same era.
Towards A Rigorous Science of Interpretable Machine Learning
F. Doshi-Velez and B. Kim · 2017
Cited alongside, same era.
Adversarial examples for malware detection
K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. D. McDaniel · 2017
Cited alongside, same era.
Yes, machine learning can be more secure! a case study on android malware detection
A. Demontis, M. Melis, B. Biggio, D. Maiorca, D. Arp, K. Rieck, I. Corona, G. Giacinto, and F. Roli
Cited in the paper.
Malware detection in adversarial settings: Exploiting feature evolutions and confusions in android apps
W. Yang, D. Kong, T. Xie, and C. A. Gunter · 2017
Later among the works it cites.
Wild patterns: Ten years after the rise of adversarial machine learning
B. Biggio and F. Roli · 2018
Closest in time.
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
A. Huang, A. Al-Dujaili, E. Hemberg, and U.-M. O’Reilly · 2018
Closest in time.