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Book Overview & Buying
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Table Of Contents
Machine Learning Security Principles
By :
Machine Learning Security Principles
By:
Overview of this book
Businesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning.
As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references.
The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies.
This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks.
By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.
Table of Contents (19 chapters)
Preface
Part 1 – Securing a Machine Learning System
Free Chapter
Chapter 1: Defining Machine Learning Security
Chapter 2: Mitigating Risk at Training by Validating and Maintaining Datasets
Chapter 3: Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks
Part 2 – Creating a Secure System Using ML
Chapter 4: Considering the Threat Environment
Chapter 5: Keeping Your Network Clean
Chapter 6: Detecting and Analyzing Anomalies
Chapter 7: Dealing with Malware
Chapter 8: Locating Potential Fraud
Chapter 9: Defending against Hackers
Part 3 – Protecting against ML-Driven Attacks
Chapter 10: Considering the Ramifications of Deepfakes
Chapter 11: Leveraging Machine Learning for Hacking
Part 4 – Performing ML Tasks in an Ethical Manner
Chapter 12: Embracing and Incorporating Ethical Behavior
Index
