Bypassing next generation malware detectors with generative adversarial networks

In 2014, Ian Goodfellow, Yoshua Bengio, and their team, proposed a framework called the generative adversarial network (GAN). Generative adversarial networks have the ability to generate images from a random noise. For example, we can train a generative network to generate images for handwritten digits from the MNIST dataset.

Generative adversarial networks are composed of two major parts: a generator and a discriminator.

The generator

The generator takes latent samples as inputs; they are randomly generated numbers, and they are trained to generate images:

For example, to generate a handwritten digit, the generator will be a fully connected...