DBWE-Corbat: Background network traffic generation using dynamic word embedding and contrastive learning for cyber range

Abstract

Background network traffic generation is critical to replicating the real network environment in Cyber Range. But how to sufficiently extract the spatio-temporal features of traffic and generate superior background network traffic are still problems for the Cyber Range. In this paper, we propose a background network traffic generative model, DBWE-Corbat. Our solution relies on intelligent feature extraction based on the DB-WE dynamic word embedding method. Which consists of Doc2Vec and two Bidirectional Long Short-Term Memory (Bi-LSTM) layers. Specifically, first we convert the traffic feature tuple data into a static word vector. Then, we capture the spatio-temporal features of the traffic for characterization. Finally, we generate high-quality and numerous background network traffic by learning the feature distribution of small samples based on the contrastive learning model SimCSE. Extensive experiments show that our approach can generate high-quality traffic data. It meets the requirements of cyber range construction compared to other traffic generation methods.