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

• URL: http://arxiv.org/abs/2301.07474v2
• Date: Thu, 19 Jan 2023 02:43:05 GMT
• Title: Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy
• Authors: Yusuke Kawamoto and Kazumasa Miyake and Koichi Konishi and Yutaka Oiwa
• Abstract summary: We first classify the damage caused by attacks against ML-based systems, define ML-specific security, and discuss its characteristics. We enumerate all relevant assets and stakeholders and provide a general taxonomy for ML-specific threats. Finally, we classify the vulnerabilities and controls of an ML-based system in terms of each vulnerable asset in the system's entire lifecycle.
• Score: 1.2043574473965317
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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