Mastersthesis,
Building a GAN from Scratch for Synthesizing Data Samples for the Use Case of Browser Fingerprinting
University of Würzburg, (March 2022)
Abstract
With the recent rise of Machine Learning and especially Deep Learning, gathering enough data is a challenging task. Generative Adversarial Networks (GANs) have emerged to synthesize fake data from real data, by employing two neural networks that interact with each other, i.e., a discriminator and a generator, where the generator tries to trick the discriminator into classifying a fake sample as real. GANs are usually employed on images, however, this has recently been adopted in other research areas. This thesis investigates the capability of GANs to generate fake samples of network traffic, e.g., for data augmentation to balance out datasets (for intrusion detection, for example).
