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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

Summary

This chapter focuses on malware, but not malware in a single location. Most hackers realize that users don’t rely on a single machine anymore and many users employ four or even more systems to interact with your ML application. Consequently, securing just one system is sort of like locking the barn with a truly impressive lock, but then forgetting to close the shackle. It’s essential to consider the bigger picture and ensure that you have looked into securing all of the systems that a user may own, including personal systems.

The most important takeaway from this chapter is that classifying malware is a difficult process best left to security professionals. The job of the administrator, DBA, manager, data scientist, or other ML expert is to convey the potential risks to a security professional and come up with a good solution to secure the application as a whole from whatever location the user might access it.

Classifying malware means ensuring that you...

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