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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

Product type Paperback

Published in Dec 2022

Publisher Packt

ISBN-13 9781804618851

Length 450 pages

Edition 1st Edition

Languages

Python

Tools

Google cloud buildpack

Concepts

Machine Learning

Author (1):

John Paul Mueller

View More author details

Table of Contents (19) Chapters

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

Why subscribe?

18. Other Books You May Enjoy

Further reading

The following bullets provide you with some additional reading that you may find useful for understanding the materials in this chapter in greater depth:

Read additional details about keeping PII out of your dataset: Data publication: Removing identifiers from data (https://libguides.library.usyd.edu.au/datapublication/desensitise-data)
Understand the need for dimensionality reduction: Machine learning: What is dimensionality reduction? (https://bdtechtalks.com/2021/05/13/machine-learning-dimensionality-reduction/)
Locate a dataset that’s safe to use for your next experiment: 10 Great Places to Find Free Datasets for Your Next Project (https://careerfoundry.com/en/blog/data-analytics/where-to-find-free-datasets/)
Gain more insights into why machine learning applications are unlikely to be fair anytime in the near future: Can Machine Learning Ever Be “Fair” — and What Does That Even Mean? (https://towardsdatascience.com...

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Authors (1)

John Paul Mueller

John Paul Mueller

John Paul Mueller is a prolific freelance author and technical editor. He's covered everything from networking and home security to database management and heads-down programming.

See other products by John Paul Mueller

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