Supporting Artificial Intelligence/Machine Learning Security Workers Through an Adversarial Techniques, Tools, and Common Knowledge Framework

被引：6

作者：

Fazelnia, Mohamad ^{[1
]}

Okutan, Ahmet ^{[1
]}

Mirakhorli, Mehdi ^{[1
,2
]}

机构：

[1] Rochester Inst Technol, Global Cybersecur Inst, Rochester, NY 14623 USA

[2] Rochester Inst Technol, Dept Software Engn, Rochester, NY 14623 USA

来源：

IEEE SECURITY & PRIVACY | 2023年 / 21卷 / 01期

关键词：

Security; Artificial intelligence; Computer security; Data models; Task analysis; Training; Robustness;

D O I：

10.1109/MSEC.2022.3221058

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article focuses on supporting artificial intelligence (AI)/machine learning (ML) security workers. It presents AI/ML adversarial techniques, tools, and common knowledge (AI/ML ATT & CK) framework to enable AI/ML security workers to intuitively explore offensive and defensive tactics.

引用

页码：37 / 48

页数：12

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