Semantic Scholar Open Access 2010 919 sitasi

The security of machine learning

Marco Barreno B. Nelson A. Joseph Doug J. Tygar

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (4)

Marco Barreno

B. Nelson

A. Joseph

Doug J. Tygar

Format Sitasi

APA MLA BibTeX

Barreno, M., Nelson, B., Joseph, A., Tygar, D.J. (2010). The security of machine learning. https://doi.org/10.1007/s10994-010-5188-5

Akses Cepat

Lihat di Sumber doi.org/10.1007/s10994-010-5188-5

Informasi Jurnal

Tahun Terbit: 2010
Bahasa: en
Total Sitasi: 919×
Sumber Database: Semantic Scholar
DOI: 10.1007/s10994-010-5188-5
Akses: Open Access ✓