Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning Security Principles

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

Arrow left icon
Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804618851
Length 450 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
John Paul Mueller John Paul Mueller
Author Profile Icon John Paul Mueller
John Paul Mueller
Arrow right icon
View More author details
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 3 – Protecting against ML-Driven Attacks

Up to this point in the book, someone could potentially experience security issues that rely on traditional methods that are modified to meet the demands of the ML environment. However, some advanced hackers employ ML applications specifically designed to thwart security measures through a learning process. In other words, the application has no other purpose than to somehow break into a system and achieve a particular set of hacker goals.

This section includes the following chapters:

  • Chapter 10, Considering the Ramifications of Deepfakes
  • Chapter 11, Leveraging Machine Learning against Hacking
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime