Method for multi-task learning fusion network traffic classification to address small sample labels.

ABSTRACT: In the context of the proliferated evolution of network service types and the expeditious augmentation of network resource deployment, the requisition for copious labeled datasets to facilitate superior performance in traffic classification methods, particularly those hinging on deep learning, is imperative. Nonetheless, the procurement and annotation of such extensive datasets necessitate considerable temporal and human resource investments. In response to this predicament, this work introduces a methodology, termed MTEFU, leveraging a deep learning model-based multi-task learning algorithm, strategically designed to mitigate the reliance on substantial labeled training samples. Multiple classification tasks, encompassing duration, bandwidth size, and business traffic category, are incorp

SUBMITTER: Liu L 

PROVIDER: S-EPMC10827795 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature