Packet Representation

We propose a novel framework to tackle the problem of packet representation learning for various traffic classification tasks. We learn packet representation, preserving both semantic and byte patterns of each packet, and utilize contrastive loss with a sample selector to optimize the learned representations so that similar packets are closer in the latent semantic space.