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| Content Provider | Springer Nature Link |
|---|---|
| Author | Barre, Marco Nelson, Blaine Joseph, Anthony D. Tygar, J. D. |
| Copyright Year | 2010 |
| Abstract | 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. |
| Starting Page | 121 |
| Ending Page | 148 |
| Page Count | 28 |
| File Format | |
| ISSN | 08856125 |
| Journal | Machine Learning |
| Volume Number | 81 |
| Issue Number | 2 |
| e-ISSN | 15730565 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2010-05-20 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Security Adversarial learning Adversarial environments Language Translation and Linguistics Simulation and Modeling Control , Robotics, Mechatronics Computing Methodologies Artificial Intelligence (incl. Robotics) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Software |
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