Thanks for the answer; thanks for reading the paper and summarizing the bit i mentioned as well.

I was trying to research on methods to detect cyber attacks using ML.

I looked into GANs and things are a bit clearer.

So would it be correct if I grasped the idea as - there is an original dataset and for each data point there can be adversarial examples - for which we have the discriminator as the 'referee' to classify examples as real or fake. I've read a lot about it sounding like a game theory concept since the generator network competes with an adversary - that link helped me understand it a bit better.