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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

Dealing with Malware

Malware encompasses a vast array of applications that are designed to disrupt, damage, gain illegal access to, spy on, and do all sorts of other unwanted things to networks, applications, data, and users. Trying to cover every potential kind of malware in all of its various forms in a single chapter, or even a single book, is impossible. Even limiting the topic to just the detection and mitigation of malware using ML techniques is impossible. So, this chapter is more of an overview of malware with some specific examples and references you can use to find additional details. No, you won’t learn how to build your very own piece of malware for experimentation and the chapter will try to limit the potential damage to your system from any example code. A focus of this chapter is the use of safe techniques for learning the skills you need to tackle malware. With this in mind, the actual sample executable is benign, but the techniques shown are effective with any...

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