STAN: Synthetic Network Traffic Generation using Autoregressive Neural Models

Shengzhe Xu, Manish Marwah, Naren Ramakrishnan

Abstract

Deep learning models have achieved great success in recent years. However, large amounts of data are typically required to train such models. While some types of data, such as images, videos, and text, are easier to find, data in certain domains is difficult to obtain. For instance, cybersecurity applications routinely use network traffic data which organizations are reluctant to share, even internally, due to privacy reasons. An alternative is to use synthetically generated data; however, most existing data generating methods lack the ability to capture complex dependency structures that are usually prevalent in real data by assuming independence either temporally or between attributes. This paper presents our approach called STAN, Synthetic Network Traffic Generation using Autoregressive Neural models, to generate realistic synthetic network traffic data. Our novel autoregressive neural architecture captures both temporal dependence and dependence between attributes at any given time. It integrates convolutional neural layers (CNN) with mixture density layers (MDN) and softmax layers to model both continuous and discrete variables. We evaluate performance of STAN by training it on both a simulated dataset and a real network traffic data set. Multiple metrics are used to compare the generated data with real data and with data generated via several baseline methods. Finally, to answer the question -- can real network traffic data be substituted with synthetic data to train models of comparable accuracy -- we consider two commonly used models for anomaly detection in such data, and compare F1/MSE measures of models trained on real data and those on increasing proportions of generated data. The results show only a small decline in accuracy of models trained solely on synthetic data.