Training: Protecting Machine Learning Models Against Attacks
Level
AdvancedDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedTraining: Protecting Machine Learning Models Against Attacks
An advanced, hands-on course dedicated to key aspects of machine learning model security. The training combines solid theory with intensive workshops, enabling participants to understand and practically counter threats in ML environments. Attendees will learn to identify, analyze, and effectively protect models from modern attacks, gaining unique skills at the intersection of cybersecurity and artificial intelligence.
What will you learn?
- Identification of advanced attack vectors targeting ML models
- Methods to prevent manipulation of training data
- Practical techniques for securing training and inference processes
- Tools and strategies for protecting sensitive models against cyber threats
Prerequisites
- Basic knowledge of Python and ML libraries (NumPy, scikit-learn, TensorFlow/PyTorch)
Who is this training for?
AI engineers and data scientists-
ML solution architects
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Professionals responsible for deploying AI solutions in organizations
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Cybersecurity specialists
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Developers working on advanced ML models
Training Program
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Day 1: Fundamentals of ML Model Security
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Module 1: Introduction to ML ecosystem threats
- Characteristics of modern AI model attacks
- Consequences of successful breaches
- Case studies of intrusions and manipulations in real-world projects
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Module 2: Types of attacks on ML models
- Adversarial attacks: methods of generating adversarial samples
- Attacks on training data privacy
- Information leakage from trained models
- Vulnerability analysis of different ML architectures
- Attacks targeting ML infrastructure
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Module 3: Workshop – Threat identification
- Simulating attacks on sample classification and regression models
- Analyzing traces and penetration mechanisms of ML models
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Day 2: Advanced Protection Techniques
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Module 4: Methods for securing ML models
- Adversarial training techniques
- Federated learning for enhanced privacy
- Implementing obfuscation and data privacy mechanisms
- Strategies for risk reduction in ML workflows
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Module 5: Workshop – Practical model protection
- Designing resilient ML architectures
- Implementing advanced defense techniques
- Security testing of ML models
- Developing security policies for ML teams
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Module 6: Security tools and frameworks
- Overview of open-source tools for model protection
- Analysis of specialized ML cybersecurity libraries
- Automating security verification processes
- Integrating security tools with ML pipelines
