2011 AdversarialMachineLearning

• (Huang et al., 2011) ⇒ Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I.P. Rubinstein, and J. D. Tygar. (2011). “Adversarial Machine Learning.” In: Proceedings of the 4th ACM workshop on Security and artificial intelligence. ISBN:978-1-4503-1003-1 doi:10.1145/2046684.2046692

Subject Headings: Adversarial Machine Learning.

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Adversarial+Machine+Learning
• http://dl.acm.org/citation.cfm?id=2046684.2046692&preflayout=flat#citedby

Quotes

Abstract

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning --- the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 AdversarialMachineLearning	Ling Huang Anthony D. Joseph Blaine Nelson Benjamin I.P. Rubinstein J. D. Tygar			Adversarial Machine Learning				10.1145/2046684.2046692		2011