Application Layer Protocol Recognition Incorporating SENet and Transformer

Abstract

Protocol recognition technology assumes a crucial position and exerts significant influence in the domains of network communication and information security. Existing protocol recognition methods based on spatio-temporal features cannot adequately and comprehensively extract protocol features. An application layer protocol recognition method incorporating SENet channel attention and Transformer is proposed. The model focuses on spatio- temporal feature extraction of protocol data, and the model consists of a spatial feature extraction module and a time extraction module. SE blocks are added to the residual network to capture the associations between multiple channels and adaptively assign weights, so as to extract the key space features in different channels. The temporal feature extraction module is constructed by stacking the transformer encoders based on multi-head attention mechanism. This module is used to comprehensively capture temporal features of the protocol data by directly leveraging the positional information of the input data. After extracting and learning more detailed spatial features and more comprehensive temporal features, better protocol feature representation is obtained to improve protocol recognition performance. Experiments are conducted on the ISCX2012 and CSE_CIC_IDS2018 hybrid datasets, and the results show that the overall recognition accuracy of the proposed model reaches 99.20%, and the [F1] score reaches 98.99%, which are higher than those of the comparison models.

Keywords

• senet; residual network; self-attention; transformer; protocol recognition; network security