Can machine learning be secure?

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Can machine learning be secure?
Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. However, machine learning algorithms themselves can be a target of attack by a malicious adversary. This paper provides a framework for answering the question, "Can machine learning be secure?" Novel contributions of this paper include a taxonomy of different types of attacks on machine learning techniques and systems, a variety of defenses against those attacks, a discussion of ideas that are important to security for machine learning, an analytical model giving a lower bound on attacker's work function, and a list of open problems. Categories and Subject Descriptors D.4.6 [Security and Protection]: Invasive software (e.g., viruses, worms, Trojan horses); I.5.1 [Models]: Statistical; I.5.2 [Design Methodology] General Terms Security Keywords Adversarial Learning, Computer Networks, Computer Sec...
Marco Barreno, Blaine Nelson, Russell Sears, Antho
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where CCS
Authors Marco Barreno, Blaine Nelson, Russell Sears, Anthony D. Joseph, J. D. Tygar
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