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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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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

Summary

If you take anything at all away from this chapter, it should be that hackers are real people. The thought that a hacker is some mindless entity out there, somewhere, whose only goal in life is to ruin your day is a shortcut to getting in your own way when it comes to dealing with hacker-created security issues. Understanding hacker behavior, realizing that hackers attack specific targets for a reason, considering that a form of attack is designed to emphasize hacker strengths, and then tailoring a solution that your organization will actually use are all part of a strategy to thwart hacker incursions. This chapter has reviewed the hacker in a unique way to help you create better, more flexible solutions.

Chapter 10 is a different take on ML security, deepfakes. A deepfake is an output of an ML application, such as a graphic or audio file, that can fool human experts easily in many cases. You may think deepfakes are more science fiction than anything else. Yet, when you...

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