PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Misleading learners: Co-opting your spam filter
Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saina, Charles Sutton, J. D. Tygar and Kai Xai
In: Machine Learning in Cyber Trust: Security, Privacy, Reliability (2009) Springer . ISBN 978-0-387-88734-0


Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. We show how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to ren- der it useless—even if the adversary’s access is limited to only 1% of the spam training messages. We demonstrate three new attacks that successfully make the filter unusable, prevent victims from receiving specific email messages, and cause spam emails to arrive in the victim’s inbox.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
ID Code:5806
Deposited By:Charles Sutton
Deposited On:08 March 2010