Network traffic classification is a crucial research area aiming to enhance
service quality, streamline network management, and bolster cybersecurity. To
address the growing complexity of transmission encryption techniques, various
machine learning and deep learning methods have been proposed. However,
existing approaches encounter two main challenges. Firstly, they struggle with
model inefficiency due to the quadratic complexity of the widely used
Transformer architecture. Secondly, they suffer from unreliable traffic
representation because of discarding important byte information while retaining
unwanted biases. To address these challenges, we propose NetMamba, an efficient
linear-time state space model equipped with a comprehensive traffic
representation scheme. We replace the Transformer with our specially selected
and improved Mamba architecture for the networking field to address efficiency
issues. In addition, we design a scheme for traffic representation, which is
used to extract valid information from massive traffic while removing biased
information. Evaluation experiments on six public datasets encompassing three
main classification tasks showcase NetMamba's superior classification
performance compared to state-of-the-art baselines. It achieves up to 4.83\%
higher accuracy and 4.64\% higher f1 score on encrypted traffic classification
tasks. Additionally, NetMamba demonstrates excellent efficiency, improving
inference speed by 2.24 times while maintaining comparably low memory usage.
Furthermore, NetMamba exhibits superior few-shot learning abilities, achieving
better classification performance with fewer labeled data. To the best of our
knowledge, NetMamba is the first model to tailor the Mamba architecture for
networking.

网络流量分类是一个重要的研究领域，追求提升服务质量、简化网络管理和加强网络安全。NetMamba 是一个高效的线性时间状态空间模型，具备全面的流量表示方案，以应对传输加密技术不断复杂化带来的挑战，并利用改进后的 Mamba 架构取代 Transformer，实现了更高的效率和准确性。