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

Monitoring and alerting

The Using supervised learning example section of Chapter 5, Keeping Your Network Clean, shows one method for monitoring your network for unusual patterns. In this case, you monitor API calls that are coming into your network from an outside source. Previous chapters have also provided you with examples of email filtering, anomaly detection, malware detection, and fraud detection. All of these kinds of detection are helpful, but monitoring and alerting for hacker attacks, in general, is harder. The point of the sections that follow is to show that you can create a combination of detection methods to ascertain the health of your organization in general so that it becomes possible to create an alert when there is a high probability that a hacker attack is about to begin.

Considering the importance of lag

Humans don’t act instantly. Even when humans are actively engaged in something, there is a reaction time to consider. For example, try the interesting...