This article discusses the evolving threats in cloud computing and machine learning, including data poisoning which impacts the trustworthiness of ML models and compromises fraud detection systems. It emphasizes the need for robust security against API exploits, side-channel attacks, and malicious third-party libraries. As machine learning becomes increasingly integrated into cloud environments, the article highlights the importance of developing proactive defenses to protect these systems and ensure their reliability and effectiveness, especially against sophisticated threats like data poisoning.
Author:
Alfredo Oliveira, David Fiser, Nitesh Surana, Magno Oliveira, Pawan Kinger
