Summary

In recent times, human reliance on ML has grown exponentially. ML models are involved in several security-critical applications such as fraud, abuse, and other kinds of cybercrime. However, many models are susceptible to adversarial attacks, where attackers manipulate the input so as to fool the model. This chapter covered the basics of AML and the goals and strategies that attackers employ. We then discussed two popular adversarial attack methods, FGSM and PGD, along with their implementation in Python. Next, we learned about methods for manipulating text and their implementation.

Because of the importance and prevalence of ML in our lives, it is necessary for security data scientists to understand adversarial attacks and learn to defend against them. This chapter provides a solid foundation for AML and the kinds of attacks involved.

So far, we have discussed multiple aspects of ML for security problems. In the next chapter, we will pivot to a closely related topic&...