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Machine learning classifiers are vulnerable to adversarial examples -- input-specific perturbations that manipulate models' output.
Soot - a java bytecode optimization framework
R. Vallée-Rai, P. Co, E. Gagnon, L. J. Hendren, P. Lam, and V. Sundaresan · 1999
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Can Machine Learning be Secure?
M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar · 2006
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Symantec report on the underground economy
M. Fossi, E. Johnson, D. Turner, T. Mack, J. Blackbird, D. McKinney, M. K. Low, T. Adams, M. P. Laucht, and J. Gough · 2008
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Dos and don’ts of machine learning in computer security
D. Arp, E. Quiring, F. Pendlebury, A. Warnecke, F. Pierazzi, C. Wressnegger, L. Cavallaro, and K. Rieck · 2010
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A survey of mobile malware in the wild
A. P. Felt, M. Finifter, E. Chin, S. Hanna, and D. Wagner · 2011
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Adversarial Machine Learning
L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. D. Tygar · 2011
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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
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Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues
I. Corona, G. Giacinto, and F. Roli · 2013
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Detection of malicious pdf files based on hierarchical document structure
N. Šrndic and P. Laskov · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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DREBIN: effective and explainable detection of android malware in your pocket
D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, and K. Rieck · 2014
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Flowdroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps
S. Arzt, S. Rasthofer, C. Fritz, E. Bodden, A. Bartel, J. Klein, Y. L. Traon, D. Octeau, and P. D. McDaniel · 2014
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Automated software transplantation
E. T. Barr, M. Harman, Y. Jia, A. Marginean, and J. Petke · 2015
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Explaining and Harnessing Adversarial Examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Androzoo: Collecting millions of android apps for the research community
K. Allix, T. F. Bissyandé, J. Klein, and Y. Le Traon · 2016
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. C. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2016
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Adversarial machine learning at scale
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2016
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Reviewer integration and performance measurement for malware detection
B. Miller, A. Kantchelian, M. C. Tschantz, S. Afroz, R. Bachwani, R. Faizullabhoy, L. Huang, V. Shankar, T. Wu, G. Yiu, A. D. Joseph, and J. D. Tygar · 2016
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Transferability in Machine Learning: From Phenomena to Black-box Attacks using Adversarial Samples
N. Papernot, P. McDaniel, and I. Goodfellow · 2016
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Dueling network architectures for deep reinforcement learning
Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas · 2016
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Automatically evading classifiers
W. Xu, Y. Qi, and D. Evans · 2016
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T. B. Brown, D. Mané, A. Roy, M. Abadi, and J. Gilmer · 2017
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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 · 2017
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Adversarial examples for malware detection
K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. D. McDaniel · 2017
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Generating adversarial malware examples for black-box attacks based on gan
W. Hu and Y. Tan · 2017
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Lightgbm: A highly efficient gradient boosting decision tree
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu · 2017
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Universal adversarial perturbations
S. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
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Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
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Malware detection in adversarial settings: Exploiting feature evolutions and confusions in android apps
W. Yang, D. Kong, T. Xie, and C. A. Gunter · 2017
Cited alongside, same era.
Defense against Universal Adversarial Perturbations
N. Akhtar, J. Liu, and A. Mian · 2018
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Adversarial deep learning for robust detection of binary encoded malware
TESSERACT: eliminating experimental bias in malware classification across space and time
F. Pendlebury, F. Pierazzi, R. Jordaney, J. Kinder, and L. Cavallaro · 2019
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Adversarial Training for Free!
A. Shafahi, M. Najibi, A. Ghiasi, Z. Xu, J. Dickerson, C. Studer, L. S. Davis, G. Taylor, and T. Goldstein · 2019
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Adversarial Training and Robustness for Multiple Perturbations
F. Tramèr and D. Boneh · 2019
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Adversarial: Perceptual Ad Blocking Meets Adversarial Machine Learning
F. Tramèr, P. Dupré, G. Rusak, G. Pellegrino, and D. Boneh · 2019
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Universal adversarial triggers for attacking and analyzing NLP
E. Wallace, S. Feng, N. Kandpal, M. Gardner, and S. Singh · 2019
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When malware is packin’ heat; limits of machine learning classifiers based on static analysis features
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A. Al-Dujaili, A. Huang, E. Hemberg, and U.-M. O’Reilly · 2018
Cited alongside, same era.
Ember: an open dataset for training static pe malware machine learning models
H. S. Anderson and P. Roth · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Evading botnet detectors based on flows and random forest with adversarial samples
G. Apruzzese and M. Colajanni · 2018
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Synthesizing Robust Adversarial Examples
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok · 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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Robust Physical-World Attacks on Deep Learning Visual Classification
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song · 2018
Cited alongside, same era.
H. Aghakhani, F. Gritti, F. Mecca, M. Lindorfer, S. Ortolani, D. Balzarotti, G. Vigna, and C. Kruegel · 2020
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Deep reinforcement adversarial learning against botnet evasion attacks
G. Apruzzese, M. Andreolini, M. Marchetti, A. Venturi, and M. Colajanni · 2020
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Defending against universal attacks through selective feature regeneration
T. Borkar, F. Heide, and L. Karam · 2020
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Sentinet: Detecting Localized Universal Attacks against Deep Learning Systems
E. Chou, F. Tramèr, and G. Pellegrino · 2020
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L. Demetrio, S. E. Coull, B. Biggio, G. Lagorio, A. Armando, and F. Roli · 2020
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Universal adversarial perturbations of malware
R. Hou, X. Xiang, Q. Zhang, J. Liu, and T. Huang · 2020
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Probabilistic naming of functions in stripped binaries
J. Patrick-Evans, L. Cavallaro, and J. Kinder · 2020
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Intriguing properties of adversarial ml attacks in the problem space
F. Pierazzi, F. Pendlebury, J. Cortellazzi, and L. Cavallaro · 2020
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Universal Adversarial Training
A. Shafahi, M. Najibi, Z. Xu, J. Dickerson, L. S. Davis, and T. Goldstein · 2020
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Physically Realizable Adversarial Examples for LiDAR Object Detection
J. Tu, M. Ren, S. Manivasagam, M. Liang, B. Yang, R. Du, F. Cheng, and R. Urtasun · 2020
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Cassandra: Detecting trojaned networks from adversarial perturbations
X. Zhang, A. Mian, R. Gupta, N. Rahnavard, and M. Shah · 2020
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Adwind malware-as-a-service hits more than 400,000 users globally
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Aimed-rl: Exploring adversarial malware examples with reinforcement learning
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Malware-as-a-service: The 9-to-5 of organized cybercrime
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