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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

Defining current network threats

Network threats go well beyond the application level, and it’s unlikely that a single individual would provide support for every protective means that a network will require. For example, developers aren’t going to handle physical security – a security company will likely handle it. However, your ML application may interact with the physical security system by monitoring cameras and other sensors. If you think this is a little futuristic, companies such as Bosch (https://www.boschsecurity.com/xc/en/solutions/video-systems/video-analytics/) and Nelly’s Security (https://www.nellyssecurity.com/blog/articles/what-is-deep-learning-ai-and-why-is-it-important-for-video-surveillance) have products available today. An ML application can look for trends, such as an attacker who is casing a business before attempting to break in. The human monitoring the cameras may not see that the same person shows up on various nights, yet never...

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