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)
1
Part 1 – Securing a Machine Learning System
5
Part 2 – Creating a Secure System Using ML
12
Part 3 – Protecting against ML-Driven Attacks
15
Part 4 – Performing ML Tasks in an Ethical Manner

Dealing with Malware

Malware encompasses a vast array of applications that are designed to disrupt, damage, gain illegal access to, spy on, and do all sorts of other unwanted things to networks, applications, data, and users. Trying to cover every potential kind of malware in all of its various forms in a single chapter, or even a single book, is impossible. Even limiting the topic to just the detection and mitigation of malware using ML techniques is impossible. So, this chapter is more of an overview of malware with some specific examples and references you can use to find additional details. No, you won’t learn how to build your very own piece of malware for experimentation and the chapter will try to limit the potential damage to your system from any example code. A focus of this chapter is the use of safe techniques for learning the skills you need to tackle malware. With this in mind, the actual sample executable is benign, but the techniques shown are effective with any...