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Machine Learning and Artificial Intelligence Security Risk: Categorizing Attacks and Failure Modes

via LinkedIn Learning

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Learn how and why machine learning and artificial intelligence technology fails and understand ways to make these systems more secure and resilient.

Syllabus

Introduction

Machine learning security concerns
What you should know

1. Machine Learning Foundations

How systems can fail and how to protect them
Why does ML security matter
Attacks vs. unintentional failure modes
Security goals for ML: CIA

2. Intentional Failure Modes/Attacks

Perturbation attacks and AUPs
Poisoning attacks
Reprogramming neural nets
Physical domain (3D adversarial objects)
Supply chain attacks
Model inversion
System manipulation
Membership inference and model stealing
Backdoors and existing exploits

3. Unintentional Failure Modes/Intrinsic Design Flaws

Reward hacking
Side effects in reinforcement learning
Distributional shifts and incomplete testing
Overfitting/underfitting
Data bias considerations

4. Building Resilient ML

Effective techniques for building resilience in ML
ML dataset hygiene
ML adversarial training
ML access control to APIs

Conclusion

Next steps

Taught by

Diana Kelley

Reviews

4.8 rating at LinkedIn Learning based on 311 ratings

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