Adverserial Machine Learning Workshop

Overview of Machine Learning Tasks

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning
• Real-world applications of Machine Learning
• Lab 1: Deep Learning for face recognition
• Machine Learning empirical process
• Theoretical model of Machine Learning
• Application of Machine Learning in Cybersecurity
• Lab 2: Support Vector Machine for IoT malware threat hunting

The Machine Learning Threat Model

• The ML attack surface
• Adversarial capabilities
• Adversarial objectives
• ML threat modelling
• Lab 3: Identifying attack surface and threat modelling of a Deep Learning agent for traffic sign detection
• ML training in adversarial settings
• ML inference in adversarial settings
• ML Differential Privacy and model thefts
• Lab 4: Data exfiltration from a trained Decision Tree model

Adversarial Poisoning Attacks

• Adversarial poisoning attacks methodology
• Poisoning attacks techniques
• Lab 5: Poisoning attacks against a text-recognition Deep Learning agent

Adversarial Evasion Attacks

• Adversarial evasion attacks methodology
• Evasion attacks techniques
• Lab 6: Evading an object recognition Deep Learning agent

Adversarial Attacks on Malware Detection Systems

• Machine learning for malware analysis
• Poisoning and evasion attacks against ML-based malware detection systems
• Lab 7: Evading a deep convolutional neural network agent for malware detection

ML Differential Privacy and Model Thefts

• Foundations of differential privacy
• Data inference and model theft attacks
• Lab 8: Stealing an overfitted machine learning model to extract credit card information

Penetration Testing of ML models

• Penetration testing methodology of ML engines
• Input sanitization of ML models
• Payload injection attacks
• Lab 9: Input sanitization testing of a ML image recognition system using anomaly
• An adversarial learning defense reference model
• Detecting adversarial attacks
• Preventing techniques for adversarial attacks
• Preserving privacy ML models
• The ML deception Defense-in-Depth (ML-DiD) Frameworks
• Lab 10: architecting a defense in depth model in an AI-centered enterprise