Network traffic classification stands as an essential endeavor within the realms of network security and management. The recent advances in learning-based methodologies have underscored their efficacy in deducing patterns from the side-channel features of encrypted network traffic. The unpredictability of traffic bursts can result in packet loss during retransmission, thereby generating fragmented feature patterns. Unfortunately, current approaches struggle to adapt to such fragmented features, often leading to a substantial decline in performance. To surmount this challenge, this paper introduces a pre-training-based framework, denoted as Nüwa, which imputes the side-channel features of encrypted network traffic, especially focusing on the temporal attributes of missing packets within a traffic session. Firstly, we propose a word-level Sequence2Embedding (S2E) module to transform side-channel features into tokens for model pre-training, as well as a Traffic Feature Masking strategy (TFM) to simulate the original flows changes in packet loss network. Besides, we also introduce a Traffic Feature Imputation (TFI) module to restore the missing values of original traffic flows in an efficient and context-aware manner. Experiments across four diverse real-world scenarios substantiate Nüwa’s capacity to restore the performance of prevalent temporal models, while maintaining the integrity of the imputed features. Notably, Nüwa has also demonstrated an impressive resilience, even under conditions of extensive feature loss and domain adaptation. The Nüwa prototype has been made accessible to the public for further research and development (https://github.com/Timeless-zfqi/Nuwa).

The recent advances in learning-based methodologies has underscored their efficacy in deducing patterns from the side-channel features of encrypted network traffic. Nonetheless, the distribution of these features has been identified as susceptible, particularly in the expansive and intricate network topologies characteristic of the modern Internet. The unpredictability of traffic bursts can result in packet loss during retransmission, thereby generating fragmented feature patterns. Unfortunately, current approaches struggle to adapt to such fragmented features, often leading to a substantial decline in performance. To surmount this challenge, this paper introduces a pre-training-based augmentation framework, denoted as Nüwa, which imputes the side-channel features of encrypted network traffic. The crux of Nüwa lies in its ability to reconstruct the side-channel features, with a particular focus on the temporal attributes of the missing packets within a traffic session. Nüwa is comprised of a word-level Sequence2Embedding module, a Traffic Noise-based Self-supervised Pre-trained Masking Strategy, and a Traffic Side-Channel Feature Imputation Module. Experiments across four diverse real-world scenarios substantiate Nüwa's capacity to restore the performance of prevalent temporal models while maintaining the integrity of the imputed features.