Adversarial Active Learning

TitleAdversarial Active Learning
Publication TypeConference Paper
Year of Publication2014
AuthorsMiller B, Kantchelian A, Afroz S, Bachwani R, Dauber E, Huang L, Tschantz MCarl, Joseph AD, Tygar J.D.
Published inProceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (AISec '14)
Page(s)3–14
Date Published11/2014
PublisherACM
Place PublishedNew York, NY, USA
ISBN Number978-1-4503-3153-1
Keywordsactive learning, human in the loop, secure machine learning
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.

URLhttp://www.icsi.berkeley.edu/pubs/networking/adversarialactivelearning2014.pdf
DOI10.1145/2666652.2666656
ICSI Research Group

Networking and Security