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Attacks on ML Systems: From Security Risk Analysis to Attack Mitigation

Published

Author(s)

Qingtian Zou, Lan Zhang, Anoop Singhal, Xiaoyan Sun, Peng Liu

Abstract

The past several years have witnessed rapidly increasing use of machine learning (ML) systems in multiple industry sectors. Since risk analysis is one of the most essential parts of the real-world ML system protection practice, there is an urgent need to conduct systematic risk analysis of ML systems. However, it is widely recognized that the existing risk analysis frameworks and techniques, which were developed to analyze enterprise (software) systems and networks, are no longer very suitable for analyzing ML systems. In this paper, we seek to present a vision on how to address two unique ML risk analysis challenges through a new risk analysis framework. This paper intends to take the initial step to bridge the gap between the existing cyber risk analysis frameworks and an ideal ML system risk analysis framework.
Proceedings Title
Proceedings of 18th International Conference on Information and Systems Security
Volume
13674
Conference Dates
December 16-20, 2022
Conference Location
Tirupati, IN
Conference Title
18th International Conference on Information and Systems Security (ICISS 2022)

Keywords

Machine learning, Deep learning, Security analysis

Citation

Zou, Q. , Zhang, L. , Singhal, A. , Sun, X. and Liu, P. (2022), Attacks on ML Systems: From Security Risk Analysis to Attack Mitigation, Proceedings of 18th International Conference on Information and Systems Security, Tirupati, IN, [online], https://doi.org/10.1007/978-3-031-23690-7_7, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935286 (Accessed March 4, 2024)
Created December 16, 2022, Updated February 13, 2023