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

Generating malware detection features

In ML, features are the data that you use to create a model. You analyze features to look for patterns of various sorts. The Checking data validity section of Chapter 6, Detecting and Analyzing Anomalies, shows you one kind of analysis. However, in the case of the Chapter 6 example and all of the other examples in the book so far, you were viewing data that humans can easily understand. This section talks about a new kind of data hidden in the confines of malware. Consequently, you’re moving from the realm of human-recognizable data to that of machine-recognizable data. The interesting thing is that your ML model won’t care about what kind of data you use to build a model, the only need is for enough data of the right kind to build a statistically sound model to use to locate malware.

Working with a first step example

To actually work with malware, you need a system that has appropriate safety measures in place, such as a virtual...