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