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LinkedIn Learning

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

via LinkedIn Learning

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Overview

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 19 ratings

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