Generative adversarial networks (GANs) represent the most advanced example of neural networks that deep learning makes available to us in the context of cybersecurity. GANs can be used for legitimate purposes, such as authentication procedures, but they can also be exploited to violate these procedures.
In this chapter, we will look at the following topics:
- The fundamental concepts of GANs and their use in attack and defense scenarios
- The main libraries and tools for developing adversarial examples
- Attacks against deep neural networks (DNNs) via model substitution
- Attacks against intrusion detection systems (IDS) via GANs
- Attacks against facial recognition procedures using adversarial examples
We will now begin the chapter by introducing the basic concepts of GANs.