1. Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy
- Author
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Kawamoto, Yusuke, Miyake, Kazumasa, Konishi, Koichi, and Oiwa, Yutaka
- Subjects
Computer Science - Cryptography and Security ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Computer Science - Software Engineering - 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.
- Published
- 2023