Compressed Network in Network Models for Traffic Classification

Accurate traffic classification is critical for network QoS provisioning and cyberspace security. Recently, classifying different traffic using convolutional neural networks (CNN) has achieved high accuracy. However, these large CNN models have millions of parameters, which are not suitable for edge computing hardware deployment. In this work, we propose a compressed network in network (NIN) model for traffic identification. A stepwise pruning and knowledge distillation (KD) is designed for training the compressed model, which aims at reducing storage and computing resources. Our method is validated with the public ISCX VPN-nonVPN traffic dataset. Experimental results show that without degrading classification accuracy, our minimum model can save more than 50% of the number of parameters and 30% of the computation time comparing with the uncompressed NIN model. The test set average F
₁
score of 0.9805 of the minimum model is higher than that of the state-of-the-art model, which is a CNN model.