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

Considering other mitigation techniques

The Using anomaly detection versus supervised learning and Using and combining anomaly detection and signature detection sections of this chapter look at anomaly detection when combined with supervised learning techniques and signature detection. These two sections broach the topic of finding a way to create a defense in-depth strategy for your infrastructure. Developing multiple layers of detection is a strategy that most security experts see as crucial for stemming the tide of hacker attacks, at least to some extent. However, it’s also important to understand that combining ML anomaly detection with other software strategies won’t completely fix the problem because the issue is one of automation. In order to have the greatest chance of success, you need humans to help see the patterns in data creation, usage, and modification that are anomalous in nature. When considering anomaly detection, also include these human-based observations...