SCNNTraffic: A lightweight and energy-efficient traffic classification method based on spiking convolution neural networks

Encrypted traffic classification aims to extract effective representations from encrypted network data, whose content remains opaque, to identify applications or user behaviors. Existing methods mainly use pre-trained models for classification, which increases accuracy but requires high computational resources and is difficult to deploy at the edge. This paper introduces an encrypted traffic classification model utilizing Spiking Convolutional Neural Networks (SCNNs) called SCNNTraffic. SCNNTraffic employs SCNNs to capture the time-varying characteristics of network traffic, facilitating smooth and stable feature extraction. This approach achieves effective classification while significantly reducing energy consumption, highlighting its advantages in resource-constrained environments. Moreover, we introduce a cross-gating module based on spiking neurons that facilitates feature fusion and further decreases power usage. Our experimental results demonstrate that the proposed model significantly reduces both the number of network parameters and energy consumption, achieving accuracy of 98.65 $$\%$$ and 92.51 $$\%$$ on the ISCX-VPN and Non-VPN datasets, respectively.