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

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Kawamoto, Yusuke, Miyake, Kazumasa, Konishi, Koichi, Oiwa, Yutaka
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.01.2023
Subjects
Artificial intelligence
Classification
Literature reviews
Machine learning
Security
Taxonomy
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Summary: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.
ISSN:2331-8422