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.

由于虚拟私人网络（VPN）和洋葱路由器（Tor）等服务的易用性，匿名网络流量比以往任何时候都更为普遍。为满足识别和分类此类流量的需求，机器学习和深度学习解决方案已成为行业标准。然而，由于网络流量数据的高度倾斜性，高性能分类器在应用于现实世界的流量分类时往往表现不佳。先前的研究表明，通过生成合成数据可以有效缓解类别不平衡的问题，尽管这些技术中有少数被应用于匿名流量检测领域。本研究利用CTGAN、CopulaGAN、VAE和SMOTE等方法创建了可行的合成匿名网络流量样本。最终，我们将GANs、VAE、SMOTE生成数据与CIC-Darknet2020数据集中的真实流量数据合并，形成了一个综合数据集——CMU-SynTraffic-2022，以供未来关于合成数据和匿名网络流量的研究使用。