Protecting the Protector - Hardening Machine Learning Defenses Against Adversarial Attacks
Explore strategies for enhancing the resilience of machine learning models against tampering in this 50-minute Black Hat conference talk. Delve into the comparison between cloud-based and client-based models' vulnerability to attacks. Examine Windows Defender Advanced Threat Protection research, various machine learning types, and their application in endpoint protection. Investigate theoretical attack vectors on supervised models, including real-world examples of attacks on certificate reputation. Learn about diverse model approaches, feature selection, and optimization for different threat scenarios. Discover the importance of training data, model selection, and preventing data leaks. Analyze the impact of ensemble models, interpretability, and real-time monitoring in strengthening defenses. Gain key insights from recent real-world case studies to better protect machine learning models against adversarial attacks.