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ISTC-CC Abstract
Adversarial Active Learning
Proceedings of 7th ACM Workshop on Artificial Intelligence and Security (AISec’14), held in conjunction with the 21st ACM Conference on Computer and Communications, November 2014.
Brad Miller, Alex Kantchelian, Sadia Afroz, Rekha Bachwani*, Edwin Dauber^,
Ling Huang†, Michael Carl Tschantz, Anthony D. Joseph, J. Doug Tygar
UC Berkeley
* Intel Labs
^ Drexel University
† DataVisor
Active learning is an area of machine learning examining strategies for allocation of nite resources, particularly human labeling efforts and to an extent feature extraction, in situations where available data exceeds available resources. In this open problem paper, we motivate the necessity of active learning in the security domain, identify problems caused by the application of present active learning techniques in adversarial settings, and propose a framework for experimentation and implementation of active learning systems in adversarial contexts. More than other contexts, adversarial contexts particularly need active learning as ongoing attempts to evade and confuse classi ers necessitate constant generation of labels for new content to keep pace with adversarial activity. Just as traditional machine learning algorithms are vulnerable to adversarial manipulation, we discuss assumptions speci c to active learning that introduce additional vulnerabilities, as well as present vulnerabilities that are ampli ed in the active learning setting. Lastly, we present a software architecture, Security-oriented Active Learning Testbed (SALT), for the research and implementation of active learning applications in adversarial contexts.
FULL PAPER: pdf
