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

Defining dataset threats

ML depends heavily on clean data. Dataset threats are especially problematic because ML techniques require huge datasets that aren’t easily monitored. The following sections help you categorize dataset threats to make them easier to understand.

Security and data in ML

Even though many of the issues addressed in this chapter also apply to data management best practices, they take on special meaning for ML because ML relies on such huge amounts of automatically collected data. Certain entities can easily add, subtract, or modify the data without anyone knowing because it’s not possible to check every piece of data or even use automation to verify it with absolute certainty. Consequently, with ML, it’s entirely possible to have a security issue and not know about it unless due diligence is exercised to remove as many possible sources of data threats as possible.

Learning about the kinds of database threats

Dataset modification...