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

Defining Machine Learning Security

Organizations trust machine learning (ML) to perform a wide variety of tasks today because it has proven to be relatively fast, inexpensive, and effective. Unfortunately, many people really aren’t sure what ML is because television, movies, and other media tend to provide an unrealistic view of the technology. In addition, some users engage in wishful thinking or feel the technology should be able to do more. Making matters worse, even the companies who should know what ML is about hype its abilities and make the processes used to perform ML tasks opaque. Before making ML secure, it’s important to understand what ML is all about. Otherwise, the process is akin to installing home security without actually knowing what the inside of the home contains or even what the exterior of the home looks like.

Adding security to an ML application involves understanding the data analyzed by the underlying algorithm and considering the goals of the application in interacting with that data. It also means looking at security as something other than restricting access to the data and the application (although, restricting access is a part of the picture).

The remainder of this chapter talks about the requirements for working with the coding examples. It’s helpful to have the right setup on your machine so that you can be sure that the examples will run as written.

Get in touch

Obviously, I want you to be able to work with the examples, so if you run into coding issues, please be sure to contact me at [email protected].

Using the downloadable source code will also save you time and effort. With these issues in mind, this chapter discusses these topics:

  • Obtaining an overview of ML
  • Defining a need for security and choosing a type
  • Making the most of this book