Semantic Scholar Open Access 2019 1545 sitasi

Adversarial machine learning

Ling Huang A. Joseph B. Nelson Benjamin I. P. Rubinstein J. D. Tygar

Lihat Sumber DOI

Abstrak

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.

Topik & Kata Kunci

Computer Science

Penulis (5)

Ling Huang

A. Joseph

B. Nelson

Benjamin I. P. Rubinstein

J. D. Tygar

Format Sitasi

APA MLA BibTeX

Huang, L., Joseph, A., Nelson, B., Rubinstein, B.I.P., Tygar, J.D. (2019). Adversarial machine learning. https://doi.org/10.1145/2046684.2046692

Akses Cepat

Lihat di Sumber doi.org/10.1145/2046684.2046692

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 1545×
Sumber Database: Semantic Scholar
DOI: 10.1145/2046684.2046692
Akses: Open Access ✓