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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

Preface

Machine learning is the most important new technology today for getting more out of data. It can reveal patterns that aren’t obvious, for example, but it requires data – lots of it. Data gathering isn’t just about data. It affects users and requires the use of applications to clean, manipulate, and analyze the data. Scientists use machine learning to discover new techniques or to create new kinds of data, such as the generation of various kinds of art based on existing inputs or the advancement of medicine through better imaging. Businesses use machine learning to perform tasks, such as detecting credit card fraud, monitoring networks, and implementing factory processes, and to achieve all sorts of other goals where humans and AI work side-by-side.

Hackers don’t always damage data; sometimes they steal it or use it to perform social attacks on a business. Sometimes they simply want money or other goods, and machine learning offers an avenue for acquiring them. A hacker may not steal anything at all – perhaps the target is someone’s reputation. It may surprise you to learn that hackers often use machine learning applications themselves to perform a kind of dance with your machine learning-based security to overcome it. However, hackers have behavioral patterns, and knowing how to detect those patterns is important in the modern computing environment.

Obtaining data in an ethical manner is important because the very act of behaving ethically reduces the security risk associated with data. However, hackers don’t necessarily target users and their data. Perhaps they’re interested in your organization’s trade secrets or committing fraud. They might simply be interested in lurking in the background and committing mischief. So, just keeping your data secure as a means of protecting your machine learning investment isn’t enough. You need to do more.

This book helps you get the big picture from a machine learning perspective using all the latest research available on methods that hackers use to break into your system. It’s about the whole system, not just your application. You will discover techniques that help you gather data ethically and keep it safe, while also preventing all sorts of illegal access methods from even occurring. In fact, you will use machine learning as a tool to keep hackers at bay and discover their true intent for your organization.

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