In the last part of the chapter, we will introduce—for the sake of completeness—some solutions of malware detection that make use of experimental methodologies based on neural networks.
We will have a more in-depth look at the topic of deep learning techniques later on in Chapter 8, GANS – Attacks and Defenses (especially when we will talk about Generative Adversarial Networks (GANs)).
Here, we will introduce the topic to show an innovative and unconventional approach to the problem of the classification of different families of malware, which makes use of deep learning algorithms developed in a completely different field of research, such as that of image recognition using Convolutional Neural Networks (CNNs).
But before going into that, let's briefly introduce Neural Networks (NNs) and their main features...
