GANs were theorized in a famous paper that dates back to 2014 (https://arxiv.org/abs/1406.2661), written by a team of researchers including Ian Goodfellow and Yoshua Bengio, which described the potential and characteristics of a special category of adversarial processes, called GANs.
The basic idea behind GANs is simple, as they consist of putting two neural networks in competition with one another, until a balanced condition of results is achieved; however at the same time, the possibilities of using these intuitions are almost unlimited, since GANs are able to learn how to imitate and artificially reproduce any data distribution, whether it represents faces, voices, texts, or even works of art.
In this chapter, we will extend the use of GANs in the field of cybersecurity, learning how it is possible to use them to both carry out attacks (such as attacks against...
