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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 has covered a broad range of network topics, which should tell you one thing – keeping your network secure is a team effort that requires the devoted efforts of professionals in several different areas. In order to make the topic a little easier to understand, this chapter broke the requirements down into traditional protections, ML protections, real-time detection, and predictive defenses. Hackers are constantly doing three things to thwart your efforts: finding new ways to break into your network, developing ever-faster techniques, and doing the unexpected to evade your defenses. These hacker methodologies are why you must view network security as a collaboration between humans and various kinds of automation. Without augmentation, humans are hopelessly mired in detail and won’t see an attack until it has already finished and the damage is done. Despite this, automation can’t possibly deal with a hacker’s ability to perform attacks...

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