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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

Employing ML in security in the real world

The real world is ever-changing and quite messy. You may think that there is a straightforward simple solution to a problem, but it’s unlikely that the solution to any given security problem is either straightforward or simple. What you often end up with is a layering of solutions that match the requirements of your environment. Consequently, you might find that an ML application designed to detect threats is part of a solution, the flexible part that learns and makes a successful attack less likely. However, you likely need to rely on traditional security and service-based security as well. It’s also important to keep user training in mind and not neglect those small things.

The reality of ML is that it’s a tool like any other tool and not somehow a magic wand that will remove all of your security problems. If Chapter 3 shows you anything, it demonstrates that ML security exploits exist in great quantities and that...

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