Near-Optimal Evasion of Convex-Inducing Classifiers

Blaine Nelson, Benjamin Rubinstein, Ling Huang, Anthony Joseph, Shing–hon Lau, Steven Lee, Satish Rao, Anthony Tran, Doug Tygar
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:549-556, 2010.

Abstract

Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary.

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-nelson10a, title = {Near-Optimal Evasion of Convex-Inducing Classifiers}, author = {Nelson, Blaine and Rubinstein, Benjamin and Huang, Ling and Joseph, Anthony and Lau, Shing–hon and Lee, Steven and Rao, Satish and Tran, Anthony and Tygar, Doug}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {549--556}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/nelson10a/nelson10a.pdf}, url = {https://proceedings.mlr.press/v9/nelson10a.html}, abstract = {Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary.} }
Endnote
%0 Conference Paper %T Near-Optimal Evasion of Convex-Inducing Classifiers %A Blaine Nelson %A Benjamin Rubinstein %A Ling Huang %A Anthony Joseph %A Shing–hon Lau %A Steven Lee %A Satish Rao %A Anthony Tran %A Doug Tygar %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-nelson10a %I PMLR %P 549--556 %U https://proceedings.mlr.press/v9/nelson10a.html %V 9 %X Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary.
RIS
TY - CPAPER TI - Near-Optimal Evasion of Convex-Inducing Classifiers AU - Blaine Nelson AU - Benjamin Rubinstein AU - Ling Huang AU - Anthony Joseph AU - Shing–hon Lau AU - Steven Lee AU - Satish Rao AU - Anthony Tran AU - Doug Tygar BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-nelson10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 549 EP - 556 L1 - http://proceedings.mlr.press/v9/nelson10a/nelson10a.pdf UR - https://proceedings.mlr.press/v9/nelson10a.html AB - Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary. ER -
APA
Nelson, B., Rubinstein, B., Huang, L., Joseph, A., Lau, S., Lee, S., Rao, S., Tran, A. & Tygar, D.. (2010). Near-Optimal Evasion of Convex-Inducing Classifiers. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:549-556 Available from https://proceedings.mlr.press/v9/nelson10a.html.

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