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Machine Learning Security Principles

You're reading from   Machine Learning Security Principles Keep data, networks, users, and applications safe from prying eyes

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804618851
Length 450 pages
Edition 1st Edition
Languages
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Author (1):
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John Paul Mueller John Paul Mueller
Author Profile Icon John Paul Mueller
John Paul Mueller
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1 – Securing a Machine Learning System
2. Chapter 1: Defining Machine Learning Security FREE CHAPTER 3. Chapter 2: Mitigating Risk at Training by Validating and Maintaining Datasets 4. Chapter 3: Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks 5. Part 2 – Creating a Secure System Using ML
6. Chapter 4: Considering the Threat Environment 7. Chapter 5: Keeping Your Network Clean 8. Chapter 6: Detecting and Analyzing Anomalies 9. Chapter 7: Dealing with Malware 10. Chapter 8: Locating Potential Fraud 11. Chapter 9: Defending against Hackers 12. Part 3 – Protecting against ML-Driven Attacks
13. Chapter 10: Considering the Ramifications of Deepfakes 14. Chapter 11: Leveraging Machine Learning for Hacking 15. Part 4 – Performing ML Tasks in an Ethical Manner
16. Chapter 12: Embracing and Incorporating Ethical Behavior 17. Index 18. Other Books You May Enjoy

Part 4 – Performing ML Tasks in an Ethical Manner

Some ML developers somehow feel that it’s OK to use any accessible data to train models, perform testing, and create an analysis, no matter what its source might be. However, recent events show that developers of all stripes must now ensure that data is collected ethically, cleaned properly, and used correctly in a transparent manner. Not only will approaching the task in this manner keep lawsuits and regulatory implications at bay, but it also makes ML inherently more secure.

This section includes the following chapter:

  • Chapter 12, Embracing and Incorporating Ethical Behavior
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