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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

Who this book is for

Whether you’re a data scientist, researcher, or manager interested in machine learning techniques from various perspectives, you will need this book because security has already become a major headache for all three groups. The problem with most resources is that they’re written by Ph.D. candidates in a language that only they understand. This book presents security in a way that’s easy to understand and employs a host of diagrams to explain concepts to visual learners. The emphasis is on real-world examples at both theoretical and hands-on levels. You’ll find links to a wealth of examples of real-world break-ins and explanations of why and how they occurred and, most importantly, how you can overcome them.

This book does assume that you’re familiar with machine learning concepts and it helps if you already know a programming language, with an emphasis on Python knowledge. The hands-on Python code is mostly meant to provide details for data scientists and researchers who need to see security concepts in action, rather than at a more theoretical level. A few examples, such as the Pix2Pix GAN in Chapter 10, require an intermediate level of programming knowledge, but most examples are written in a manner that everyone can use.

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