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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 network traffic detection. This work compares the ability of a Conditional Tabular Generative Adversarial Network (CTGAN), Copula Generative Adversarial Network (CopulaGAN), Variational Autoencoder (VAE), and Synthetic Minority Over-sampling Technique (SMOTE) to create viable synthetic anonymous network traffic samples. Moreover, we evaluate the performance of several shallow boosting and bagging classifiers as well as deep learning models on the synthetic data. Ultimately, we amalgamate the data generated by the GANs, VAE, and SMOTE into a comprehensive dataset dubbed CMU-SynTraffic-2022 for future research on this topic. Our findings show that SMOTE consistently outperformed the other upsampling techniques, improving classifiers&#x2019; F1-scores over the control by &#x007E;7.5% for application type characterization. Among the tested classifiers, Light Gradient Boosting Machine achieved the highest F1-score of 90.3% on eight application types.

Evaluation of Synthetic Data Generation Techniques in the Domain of Anonymous Traffic Classification