Constructing the face of network data

Abstract

Network datasets are an essential part of understanding, managing, and operating modern wide-area, data-center, and cellular networks. They are involved throughout the various stages of network development, from simulations, stress testing, to machine-learning training (for anomaly-based intrusion detection systems) and more. Despite the need, network datasets are rare due to concerns related to information privacy and sensitivity. In this paper, we aim to tackle this challenge and put forth a method, based on Generative Adversarial Networks (GANs), for generating new (and timely) datasets, automatically, that are provisioned as complete raw packets traces of a network and not just feature values.