ODTC: An online darknet traffic classification model based on multimodal self-attention chaotic mapping features

Abstract

Darknet traffic classification is significantly important to network management and security. To achieve fast and accurate classification performance, this paper proposes an online classification model based on multimodal self-attention chaotic mapping features. On the one hand, the payload content of the packet is input into the network integrating CNN and BiGRU to extract local space-time features. On the other hand, the flow level abstract features processed by the MLP are introduced. To make up for the lack of the indistinct feature learning, a feature amplification module that uses logistic chaotic mapping to amplify fuzzy features is introduced. In addition, a multi-head attention mechanism is used to excavate the hidden relationships between different features. Besides, to better support new traffic classes, a class incremental learning model is developed with the weighted loss function to achieve continuous learning with reduced network parameters. The experimental results on the public CICDarketSec2020 dataset show that the accuracy of the proposed model is improved in multiple categories; however, the time and memory consumption is reduced by about 50. Compared with the existing state-of-the-art traffic classification models, the proposed model has better classification performance.

Keywords

• darknet traffic
• deep learning
• logistic chaos map
• multi-head self-attention mechanism
• online parameter update