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Protecting the Protector - Hardening Machine Learning Defenses Against Adversarial Attacks

Black Hat via YouTube

Overview

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This course focuses on strategies to enhance the resilience of machine learning models against adversarial attacks. It covers topics such as types of machine learning, theoretical attack vectors, diverse models, model selection, data leaks, experiment design, real-time monitoring, and the impact of ensemble models. The course aims to teach participants how to protect machine learning systems from tampering and compare the security challenges of cloud-based and client-based models. The intended audience for this course includes individuals interested in cybersecurity, machine learning, and data protection.

Syllabus

Intro
Windows Defender Advanced Threat Protection
Windows Defender ATP Research
Types of Machine Learning
Machine Learning for Endpoint Protection
Client Machine Learning
Cloud Machine Learning
Theoretical Attack Vectors: Supervised Model
Attacks on Certificate Reputation (Early 2017)
Attacks on Certificate Reputation (cont.)
Challenges
Diverse Models 1. Different feature sets
Features - Highly dimensional data
Diverse Set of Classifiers Feature Set PE Properties
Optimizing for Different Threat Scenarios
Boolean Stacking TRAINING DATA
Model Selection
Data Leaks
Using Unsupervised Features
Experiment Design Supervised Training
What if ... Attacker crafts adversarial samples to flip verdicts SAMPLES
Realtime Monitoring
Impact of Ensemble Models
Bonus: Interpretability
Benefits of an Ensemble Model
Recent Realworld Case Studies (2)
Key Takeaways

Taught by

Black Hat

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