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Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection.
Adversarial classification
Dalvi, N., Domingos, P., Mausam, Sanghai, S., and Verma, D · 2004
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Stochastic Local Search : Foundations & Applications
Hoos, H. H., and Stutzle, T · 2004
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Adversarial learning
Lowd, D., and Meek, C · 2005
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Can machine learning be secure?
Barreno, M., Nelson, B., Sears, R., Joseph, A. D., and Tygar, J. D · 2006
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Evading network anomaly detection systems: Formal reasoning and practical techniques
Fogla, P., and Lee, W · 2006
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Polymorphic blending attacks
Fogla, P., Sharif, M., Perdisci, R., Kolesnikov, O., and Lee, W · 2006
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Convex learning with invariances
Teo, C. H., Globerson, A., Roweis, S., and Smola, A. J · 2007
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Robustness and regularization of support vector machines
Xu, H., Caramanis, C., and Mannor, S · 2009
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Detection and analysis of drive- by-download attacks and malicious javascript code
Cova, M., Kruegel, C., and Vigna, G · 2010
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Stackelberg games for adversarial prediction problems
Brückner, M., and Scheffer, T · 2011
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Static prediction games for adversarial learning problems
Brückner, M., and Scheffer, T · 2012
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Cuckoo sandbox: A malware analysis system, 2012
Guarnieri, C., Tanasi, A., Bremer, J., and Schloesser, M · 2012
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Query strategies for evading convex-inducing classifiers
Nelson, B., Rubinstein, B. I., Huang, L., Joseph, A. D., Lee, S. J., Rao, S., and Tygar, J · 2012
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Malicious pdf detection using matadata and structural features
Smutz, C., and Stavrou, A · 2012
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Malicious pdf detection using matadata structural features
Smutz, C., and Stavrou, A · 2012
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Adversarial support vector machine learning
Zhou, Y., Kantarcioglu, M., Thuraisingham, B. M., and Xi, B · 2012
Cited alongside, same era.
Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Srndic, N., Laskov, P., Giacinto, G., and Roli, F · 2013
Cited alongside, same era.
Looking at the bag is not enough to find the bomb: an evasion of structural methods for malicious PDF files detection
Maiorca, D., Corona, I., and Giacinto, G · 2013
Cited alongside, same era.
Detection of malicious PDF files based on hierarchical document structure
Šrndic, N., and Laskov, P · 2013
Cited alongside, same era.
Security evaluation of pattern classifiers under attack
Biggio, B., Fumera, G., and Roli, F · 2014
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget, J., Mirza, M., Xu, B., Warde, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Hidost: a static machine-learning-based detector of malicious files
Šrndić, N., and Laskov, P · 2016
Later among the works it cites.
Automatically evading classifiers: A case study on PDF malware classifiers
Xu, W., Qi, Y., and Evans, D · 2016
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Towards evaluating the robustness of neural networks
Carlini, N., and Wagner, D · 2017
Closest in time.
Adversarial perturbations against deep neural networks for malware classification
Grosse, K., Papernot, N., Manoharan, P., Backes, M., and McDaniel, P · 2017
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Pdfrw: A pure python library that reads and writes pdfs
Maupin, P · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
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Cited alongside, same era.
Practical evasion of a learning-based classifier: A case study
Šrndic, N., and Laskov, P · 2014
Cited alongside, same era.
Optimal randomized classification in adversarial settings
Vorobeychik, Y., and Li, B · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Cited alongside, same era.
Adversarial feature selection against evasion attacks
Zhang, F., Chan, P., Biggio, B., Yeung, D., and Roli, F · 2015
Cited alongside, same era.
Learning with a strong adversary
Huang, R., Xu, B., Schuurmans, D., and Szepesvári, C · 2016
Cited alongside, same era.
Evasion and hardening of tree ensemble classifiers
Kantchelian, A., Tygar, J. D., and Joseph, A. D · 2016
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Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2018
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Evasion-robust classification on binary domains
Li, B., and Vorobeychik, Y · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Towards the science of security and privacy in machine learning
Papernot, N., McDaniel, P., Sinha, A., and Wellman, M · 2018
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Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 2018
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Adversarial Machine Learning
Vorobeychik, Y., and Kantarcioglu, M · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E., and Kolter, J. Z · 2018
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Feature cross-substitution in adversarial classification
Li, B., and Vorobeychik, Y · 2095
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