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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

Monitoring and alerting

The Using supervised learning example section of Chapter 5, Keeping Your Network Clean, shows one method for monitoring your network for unusual patterns. In this case, you monitor API calls that are coming into your network from an outside source. Previous chapters have also provided you with examples of email filtering, anomaly detection, malware detection, and fraud detection. All of these kinds of detection are helpful, but monitoring and alerting for hacker attacks, in general, is harder. The point of the sections that follow is to show that you can create a combination of detection methods to ascertain the health of your organization in general so that it becomes possible to create an alert when there is a high probability that a hacker attack is about to begin.

Considering the importance of lag

Humans don’t act instantly. Even when humans are actively engaged in something, there is a reaction time to consider. For example, try the interesting...

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