Learning in a Changing and Adversarial Environment

We will discuss and explore the topics of adversarial machine learning (AML) in the spirit of completing the big picture of AI/machine learning (ML) by considering its security and privacy perspectives, which become more and more challenging and critical nowadays with the popularity of generative AI (GenAI). In previous chapters, we learned about the fundamentals of ML and a variety of applications in cybersecurity. However, the nature of ML brings native vulnerabilities that can be exploited by highly technical adversaries. Therefore, AML is a rising research area that draws a lot of attention from both academics and industries. Its objective is to study the vulnerabilities of ML proactively and to design and devise novel approaches that are more robust and reliable than traditional ML methods.

In this chapter, you will learn about the concepts and motivation of AML, and explore the differences between AML and traditional ML algorithms...