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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

Locating Potential Fraud

Fraud is deception that is perpetrated with either financial or personal gain in mind. Many people think about identity theft or credit card theft when thinking about fraud, but that’s only a very small part of the picture. A person claiming or being credited with another’s accomplishments or qualities is another form of fraud. The key word when thinking about fraud is deception, which takes many forms – for example, disinformation and hypocrisy. Consequently, it’s important to come up with a solid definition of what fraud is when working with ML applications and data, which is the goal of the first part of this chapter.

Determining fraud sources is essential with ML because fraud sources generate data – deceptive data. Yes, the data looks just fine, but when you study it in depth, it contains one or more of the five mistruths of data described in the Defining the human element section of Chapter 1, Defining Machine Learning...

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