Fundamentals of Reverse Engineering ML Training by Tonex
This professional training program offers a deep dive into the essential methodologies and practices of reverse engineering machine learning (ML) models. Designed to bridge the gap between AI model transparency and robust security evaluation, it equips learners with practical strategies to understand, deconstruct, and assess machine learning algorithms. From interpreting model logic to understanding adversarial impacts, the course enhances participants’ ability to analyze ML behavior critically. Cybersecurity professionals particularly benefit, as the training emphasizes the identification of vulnerabilities in ML pipelines, which are increasingly targeted in modern cyberattacks. This proactive approach helps secure AI systems against model theft, poisoning, and inference attacks.
Audience:
- Cybersecurity professionals
- Data scientists
- AI/ML engineers
- Security analysts
- Software developers
- Compliance officers
- Threat intelligence teams
- IT risk management personnel
Learning Objectives:
- Understand core principles of ML reverse engineering
- Identify common ML architecture vulnerabilities
- Analyze input-output behavior of black-box models
- Assess risks related to model inversion and extraction
- Apply techniques to deconstruct trained ML systems
- Strengthen AI security through forensic inspection
Course Modules:
Module 1: Introduction to ML Reverse Engineering
- Overview of reverse engineering concepts
- Evolution of reverse engineering in ML
- Importance in adversarial security context
- Key terms and methodologies
- Threat models targeting ML systems
- Ethical and legal considerations
Module 2: Understanding ML Architectures
- Classification vs regression models
- Deep learning vs classical ML
- Components of ML pipelines
- Preprocessing and feature extraction
- Model training and optimization
- ML deployment environments
Module 3: Techniques in Reverse Engineering
- Black-box model analysis
- Query-based attack methods
- Model behavior approximation
- Input-output correlation mapping
- Surrogate model construction
- Output pattern interpretation
Module 4: Model Inversion & Extraction
- Fundamentals of model inversion attacks
- Model stealing techniques
- Shadow model development
- Confidence score exploitation
- Extracting training data insights
- Mitigation strategies
Module 5: Adversarial Impacts & Risks
- Evasion and poisoning threats
- AI supply chain vulnerabilities
- Adversarial input crafting
- Risk assessment frameworks
- Security posture evaluation
- Real-world attack case studies
Module 6: Countermeasures & Security Practices
- Differential privacy in ML
- Output obfuscation techniques
- Rate limiting and access control
- Defensive model hardening
- Monitoring anomalous queries
- Building resilient AI systems
Empower your security strategy with Tonex’s Fundamentals of Reverse Engineering ML Training. Gain the knowledge to dissect, analyze, and secure ML systems against emerging cyber threats. Enroll today to protect your AI-driven assets and stay ahead in the evolving threat landscape.