CMU-SynTraffic-2022

Anonymous network traffic is more pervasive than ever due to the accessibility of services such as virtual private networks (VPN) and The Onion Router (Tor). To address the need to identify and classify this traffic, machine and deep learning solutions have become the standard. However, high-performing classifiers often scale poorly when applied to real-world traffic classification due to the heavily skewed nature of network traffic data. Prior research has found synthetic data generation to be effective at alleviating concerns surrounding class imbalance, though a limited number of these techniques have been applied to the domain of anonymous traffic detection. A CTGAN, CopulaGAN, VAE, and SMOTE were utilized to create viable synthetic anonymous network traffic samples. Ultimately, we amalgamate the data generated by the GANs, VAE, SMOTE, and real traffic from the CIC-Darknet2020 dataset into a comprehensive dataset, CMU-SynTraffic-2022, for future research on synthetic data and anonymous network traffic.