Book Image

Machine Learning Security Principles

By : John Paul Mueller
Book Image

Machine Learning Security Principles

By: John Paul Mueller

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)
Part 1 – Securing a Machine Learning System
Part 2 – Creating a Secure System Using ML
Part 3 – Protecting against ML-Driven Attacks
Part 4 – Performing ML Tasks in an Ethical Manner


If you take anything at all away from this chapter, it should be that hackers are real people. The thought that a hacker is some mindless entity out there, somewhere, whose only goal in life is to ruin your day is a shortcut to getting in your own way when it comes to dealing with hacker-created security issues. Understanding hacker behavior, realizing that hackers attack specific targets for a reason, considering that a form of attack is designed to emphasize hacker strengths, and then tailoring a solution that your organization will actually use are all part of a strategy to thwart hacker incursions. This chapter has reviewed the hacker in a unique way to help you create better, more flexible solutions.

Chapter 10 is a different take on ML security, deepfakes. A deepfake is an output of an ML application, such as a graphic or audio file, that can fool human experts easily in many cases. You may think deepfakes are more science fiction than anything else. Yet, when you...