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...

• Type: Conference
• Year: 2006
• Where: CCS
• Authors: Marco Barreno, Blaine Nelson, Russell Sears, Anthony D. Joseph, J. D. Tygar
• Keywords: CCS 2006, Machine Learning, Machine Learning Algorithms, Machine Learning Systems, Security Privacy