Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy

Authors: Yusuke Kawamoto, Kazumasa Miyake, Koichi Konishi, Yutaka Oiwa
Published: 2023-01-18 | Updated: 2023-01-19

Source: https://arxiv.org/abs/2301.07474

PDF: https://arxiv.org/pdf/2301.07474

Labels Predicted by AI

ポイズニング メンバーシップ推論 脆弱性管理

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

Abstract

In this article, we propose the Artificial Intelligence Security Taxonomy to systematize the knowledge of threats, vulnerabilities, and security controls of machine-learning-based (ML-based) systems. We first classify the damage caused by attacks against ML-based systems, define ML-specific security, and discuss its characteristics. Next, we enumerate all relevant assets and stakeholders and provide a general taxonomy for ML-specific threats. Then, we collect a wide range of security controls against ML-specific threats through an extensive review of recent literature. Finally, we classify the vulnerabilities and controls of an ML-based system in terms of each vulnerable asset in the system’s entire lifecycle.