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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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Toc

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

Considering other mitigation techniques

The Using anomaly detection versus supervised learning and Using and combining anomaly detection and signature detection sections of this chapter look at anomaly detection when combined with supervised learning techniques and signature detection. These two sections broach the topic of finding a way to create a defense in-depth strategy for your infrastructure. Developing multiple layers of detection is a strategy that most security experts see as crucial for stemming the tide of hacker attacks, at least to some extent. However, it’s also important to understand that combining ML anomaly detection with other software strategies won’t completely fix the problem because the issue is one of automation. In order to have the greatest chance of success, you need humans to help see the patterns in data creation, usage, and modification that are anomalous in nature. When considering anomaly detection, also include these human-based observations...

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