CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method.

ABSTRACT: With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteris

SUBMITTER: Hu X 

PROVIDER: S-EPMC8705865 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature