期刊
MACHINE LEARNING
卷 81, 期 2, 页码 121-148出版社
SPRINGER
DOI: 10.1007/s10994-010-5188-5
关键词
Security; Adversarial learning; Adversarial environments
资金
- National Science Foundation (NSF) [CCF-0424422]
- AFOSR [FA9550-06-1-0244]
- BT
- Cisco
- DoCoMo USA Labs
- EADS
- ESCHER
- HP
- IBM
- iCAST
- Intel
- Microsoft
- ORNL
- Pirelli
- Qualcomm
- Sun
- Symantec
- TCS
- Telecom Italia
- United Technologies
- California state Microelectronics Innovation and Computer Research Opportunities [06-148, 07-012]
- Amazon Web Services
Machine learning's ability to rapidly evolve to changing and complex situations has helped it become a fundamental tool for computer security. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine learning systems. We show how these classes influence the costs for the attacker and defender, and we give a formal structure defining their interaction. We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing how it can guide attacks against SpamBayes, a popular statistical spam filter. Finally, we discuss how our taxonomy suggests new lines of defenses.
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