Modeling and Security Analysis of Attacks on Machine Learning Systems
Anoop Singhal · 2024
The past several years have witnessed rapidly increasing use of machine learning (ML) systems in multiple industry sectors. Since security analysis is one of the most essential parts of the real-world ML system protection practice, there is an urgent need to conduct systematic security analysis of ML systems. However, it is widely recognized that the existing security analysis approaches 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 present a methodology for ML-system-specific security analysis.