Poisoning Attacks against Support Vector Machines

Comparison of learning algorithms for handwritten digit recognition

LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Müller, U., Säckinger, E., Simard, P., and Vapnik, V · 1995

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A sense of self for unix processes

Forrest, Stephanie, Hofmeyr, Steven A., Somayaji, Anil, and Longstaff, Thomas A · 1996

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Handbook of matrices

Lütkepohl, Helmut · 1996

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Incremental and decremental support vector machine learning

Cauwenberghs, Gert and Poggio, Tomaso · 2001

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Statistical fraud detection: A review

Bolton, Richard J. and Hand, David J · 2002

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A behavior-based approach to securing email systems

Stolfo, Salvatore J., Hershkop, Shlomo, Wang, Ke, Nimeskern, Olivier, and Hu, Chia-Wei · 2003

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SpamBayes: Effective open-source, Bayesian based, email classification system

Meyer, Tony A. and Whateley, Brendon · 2004

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Can machine learning be secure?

Barreno, Marco, Nelson, Blaine, Sears, Russell, Joseph, Anthony D., and Tygar, J. D · 2006

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Nightmare at test time: Robust learning by feature deletion

Globerson, A. and Roweis, S · 2006

Cited alongside, same era.

Exploiting machine learning to subvert your spam filter

Nelson, Blaine, Barreno, Marco, Chi, Fuching Jack, Joseph, Anthony D., Rubinstein, Benjamin I. P., Saini, Udam, Sutton, Charles, Tygar, J. D., and Xia, Kai · 2008

Cited alongside, same era.

Convex learning with invariances

Teo, C.H., Globerson, A., Roweis, S., and Smola, A · 2008

Cited alongside, same era.

Nash equilibria of static prediction games

Brückner, Michael and Scheffer, Tobias · 2009

Cited alongside, same era.

ANTIDOTE: Understanding and defending against poisoning of anomaly detectors

Rubinstein, Benjamin I. P., Nelson, Blaine, Huang, Ling, Joseph, Anthony D., hon Lau, Shing, Rao, Satish, Taft, Nina, and Tygar, J. D · 2009

Multiple classifier systems for robust classifier design in adversarial environments

Biggio, Battista, Fumera, Giorgio, and Roli, Fabio · 2010

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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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Learning to classify with missing and corrupted features

Dekel, O., Shamir, O., and Xiao, L · 2010

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Online anomaly detection under adversarial impact

Kloft, Marius and Laskov, Pavel · 2010

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Cujo: Efficient detection and prevention of drive-by-download attacks

Rieck, K., Krüger, T., and Dewald, A · 2010

Later among the works it cites.

ZOZZLE: Fast and precise in-browser JavaScript malware detection

Curtsinger, C., Livshits, B., Zorn, B., and Seifert, C · 2011