Abstract: Video streaming traffic has been dominating the global network and the challenges have exacerbated with the gaining popularity of interactive videos, a.k.a.360 videos, as they require more network resources. However, effective provision of network resources for video streaming traffic is problematic due to the inability to identify video traffic flows through the network because of end-to-end encryption. Despite the promise given for network security and privacy, end-to-end encryption also provides a shield for adversaries. To this end, encrypted traffic classification and content fingerprinting with advanced Machine Learning (ML) methods have been proposed. Nevertheless, achieving high performance requires a significant amount of training data, which is a challenging task in operational networks due to the sheer volume of traffic and privacy concerns. As a solution, in this paper, we propose a novel Generative Adversarial Network (GAN) based data generation solution to synthesize video streaming data for two different tasks, 360/normal video classification and video fingerprinting. The solution consists of a percentile-based data mapping mechanism to enhance the data generation process, which is further supported by novel algorithms for data pre-processing and GAN model training. Taking over 6600 actual video traces and generating over 150,000 new traces, our ML-based traffic classification results show a 5–16% of accuracy improvement in both tasks.