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

Who this book is for

Whether you’re a data scientist, researcher, or manager interested in machine learning techniques from various perspectives, you will need this book because security has already become a major headache for all three groups. The problem with most resources is that they’re written by Ph.D. candidates in a language that only they understand. This book presents security in a way that’s easy to understand and employs a host of diagrams to explain concepts to visual learners. The emphasis is on real-world examples at both theoretical and hands-on levels. You’ll find links to a wealth of examples of real-world break-ins and explanations of why and how they occurred and, most importantly, how you can overcome them.

This book does assume that you’re familiar with machine learning concepts and it helps if you already know a programming language, with an emphasis on Python knowledge. The hands-on Python code is mostly meant to provide details for data scientists and researchers who need to see security concepts in action, rather than at a more theoretical level. A few examples, such as the Pix2Pix GAN in Chapter 10, require an intermediate level of programming knowledge, but most examples are written in a manner that everyone can use.