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

Summary

This chapter has provided you with the barest of overviews of deepfakes and the technologies used to create them: autoencoders and GANs. What you should take away from this chapter is the knowledge that these technologies are simply tools that someone can use for good or evil intent. From a security perspective, using deepfakes can help harden your surveillance technologies and help you implement better facial recognition strategies. Of course, you also have to be wary of hackers who modify your models, damage your data, or try to sway the output of your models in a way that is beneficial to them using other methods.

Chapter 11 is going to move further into the security realm of GANs by looking at ways in which they are used by hackers to gain entry into your systems or by you to thwart hacker advances. The fact that GANs can learn from each experience means that the wall-building security strategies of the past have taken on a new aspect. The machines are not merely hosts...