TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences
Journal:
arXiv
Published Date:
Jun 17, 2025
Abstract
Traffic classification has a significant impact on maintaining the Quality of
Service (QoS) of the network. Since traditional methods heavily rely on feature
extraction and large scale labeled data, some recent pre-trained models manage
to reduce the dependency by utilizing different pre-training tasks to train
generic representations for network packets. However, existing pre-trained
models typically adopt pre-training tasks developed for image or text data,
which are not tailored to traffic data. As a result, the obtained traffic
representations fail to fully reflect the information contained in the traffic,
and may even disrupt the protocol information. To address this, we propose
TraGe, a novel generic packet representation model for traffic classification.
Based on the differences between the header and payload-the two fundamental
components of a network packet-we perform differentiated pre-training according
to the byte sequence variations (continuous in the header vs. discontinuous in
the payload). A dynamic masking strategy is further introduced to prevent
overfitting to fixed byte positions. Once the generic packet representation is
obtained, TraGe can be finetuned for diverse traffic classification tasks using
limited labeled data. Experimental results demonstrate that TraGe significantly
outperforms state-of-the-art methods on two traffic classification tasks, with
up to a 6.97% performance improvement. Moreover, TraGe exhibits superior
robustness under parameter fluctuations and variations in sampling
configurations.
