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


This chapter focuses on malware, but not malware in a single location. Most hackers realize that users don’t rely on a single machine anymore and many users employ four or even more systems to interact with your ML application. Consequently, securing just one system is sort of like locking the barn with a truly impressive lock, but then forgetting to close the shackle. It’s essential to consider the bigger picture and ensure that you have looked into securing all of the systems that a user may own, including personal systems.

The most important takeaway from this chapter is that classifying malware is a difficult process best left to security professionals. The job of the administrator, DBA, manager, data scientist, or other ML expert is to convey the potential risks to a security professional and come up with a good solution to secure the application as a whole from whatever location the user might access it.

Classifying malware means ensuring that you...