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Machine learning-based systems for malware detection operate in a hostile environment.
Greedy function approximation: A gradient boosting machine
J. H. Friedman · 2000
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Mimicry attacks on host based intrusion detection systems
D. Wagner and P. Soto · 2002
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Efficient black-box optimization of adversarial windows malware with constrained manipulations
L. Demetrio, B. Biggio, G. Lagorio, F. Roli, and A. Armando · 2003
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Dynamic analysis of malicious code
U. Bayer, A. Moser, C. Kruegel, and E. Kirda · 2006
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Polymorphic blending attacks
P. Fogla, M. Sharif, R. Perdisci, O. Kolesnikov, and W. Lee · 2006
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Learning to detect and classify malicious executables in the wild
J. Z. Kolter and M. A. Maloof · 2006
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Automated classification and analysis of internet malware
M. Bailey, J. Oberheide, J. Andersen, Z. M. Mao, F. Jahanian, and J. Nazario · 2007
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Limits of static analysis for malware detection
A. Moser, C. Kruegel, and E. Kirda · 2007
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On the infeasibility of modeling polymorphic shellcode
Y. Song, M. E. Locasto, A. Stavrou, and S. J. Stolfo · 2007
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Toward automated dynamic malware analysis using cwsandbox
C. Willems, T. Holz, and F. Freiling · 2007
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L. Demetrio, S. E. Coull, B. Biggio, G. Lagorio, A. Armando, and F. Roli · 2008
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Mcboost: Boosting scalability in malware collection and analysis using statistical classification of executables
R. Perdisci, A. Lanzi, and W. Lee · 2008
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Learning and classification of malware behavior
K. Rieck, T. Holz, C. Willems, P. Düssel, and P. Laskov · 2008
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Scalable, behavior-based malware clustering
U. Bayer, P. M. Comparetti, C. Hlauschek, C. Krügel, and E. Kirda · 2009
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Effective and efficient malware detection at the end host
C. Kolbitsch, P. M. Comparetti, C. Kruegel, E. Kirda, X. yong Zhou, and X. Wang · 2009
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Accessminer: using system-centric models for malware protection
A. Lanzi, D. Balzarotti, C. Kruegel, M. Christodorescu, and E. Kirda · 2010
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Wunderwuzzi23 · 2010
Cited alongside, same era.
Support vector machines under adversarial label noise
B. Biggio, B. Nelson, and P. Laskov · 2011
Cited alongside, same era.
Bitshred: Feature hashing malware for scalable triage and semantic analysis
J. Jang, D. Brumley, and S. Venkataraman · 2011
Cited alongside, same era.
Abusing file processing in malware detectors for fun and profit
S. Jana and V. Shmatikov · 2012
Cited alongside, same era.
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
Cited alongside, same era.
Drebin: Effective and explainable detection of android malware in your pocket
Learning to evade static PE machine learning malware models via reinforcement learning
H. S. Anderson, A. Kharkar, B. Filar, D. Evans, and P. Roth · 2018
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Wild patterns: Ten years after the rise of adversarial machine learning
B. Biggio and F. Roli · 2018
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Adversarially robust malware detection using monotonic classification
I. Íncer, M. Theodorides, S. Afroz, and D. Wagner · 2018
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Adversarial malware binaries: Evading deep learning for malware detection in executables
B. Kolosnjaji, A. Demontis, B. Biggio, D. Maiorca, G. Giacinto, C. Eckert, and F. Roli · 2018
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Trojaning attack on neural networks
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang · 2018
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D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, and K. Rieck · 2014
Cited alongside, same era.
Practical evasion of a learning-based classifier: A case study
N. Šrndić and P. Laskov · 2014
Cited alongside, same era.
XGBoost: A scalable tree boosting system
T. Chen and C. Guestrin · 2016
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
N. Papernot, P. McDaniel, and I. Goodfellow · 2016
Cited alongside, same era.
Stealing machine learning models via prediction apis
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
Cited alongside, same era.
Automatically evading classifiers: A case study on pdf malware classifiers
W. Xu, Y. Qi, and D. Evans · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. A. Wagner · 2017
Cited alongside, same era.
Forgotten siblings: Unifying attacks on machine learning and digital watermarking
E. Quiring, D. Arp, and K. Rieck · 2018
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Shallow security: On the creation of adversarial variants to evade machine learning-based malware detectors
F. Ceschin, M. Botacin, H. M. Gomes, L. S. Oliveira, and A. Grégio · 2019
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Misleading authorship attribution of source code using adversarial learning
E. Quiring, A. Maier, and K. Rieck · 2019
Later among the works it cites.
Seeing is not believing: Camouflage attacks on image scaling algorithms
Q. Xiao, Y. Chen, C. Shen, Y. Chen, and K. Li · 2019
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Intriguing properties of adversarial ML attacks in the problem space
F. Pierazzi, F. Pendlebury, J. Cortellazzi, and L. Cavallaro · 2020
Closest in time.
Adversarial preprocessing: Understanding and preventing image-scaling attacks in machine learning
E. Quiring, D. Klein, D. Arp, M. Johns, and K. Rieck · 2020
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2020 machine learning security evasion competition
H. Anderson · 2020
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Adversarial malware in machine learning detectors: Our mlsec 2020’s secrets
F. Ceschin and M. Botacin · 2020
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Evading machine learning malware classifiers
W. Fleshman · 2020
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Machine learning security evasion competition 2020 invites researchers to defend and attack
J. Stanley · 2020
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