NDLI: Adversarial Active Learning

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Adversarial Active Learning

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Adversarial Active Learning

Content Provider	ACM Digital Library
Author	Dauber, Edwin Tygar, J.D. Afroz, Sadia Joseph, Anthony D. Huang, Ling Miller, Brad Tschantz, Michael Carl Bachwani, Rekha Kantchelian, Alex
Abstract	Active learning is an area of machine learning examining strategies for allocation of finite 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 classifiers 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 specific to active learning that introduce additional vulnerabilities, as well as present vulnerabilities that are amplified 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.
Starting Page	3
Ending Page	14
Page Count	12
File Format	PDF
ISBN	9781450331531
DOI	10.1145/2666652.2666656
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Active learning Human in the loop Secure machine learning
Content Type	Text
Resource Type	Article

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