Fusion Dilated CNN for Encrypted Web Traffic Classification

A Growing number of conventional Convolutional neural network (CNN) models have been employed for encrypted web traffic characterization. However, the application of CNN models is confronted with two significant challenges; a) they possess short reflective fields that don’t gather much-encrypted traffic information for effective and accurate predictions. b) these models are not adaptive to the diverse nature of traffic flow because of their singlehead architecture. This paper alleviates these problems using the fusion of dilated Convolutional neural networks dubbed FDCNN. FDCNN architecture supports exponentially large receptive fields and captures local dependencies in encrypted traffic data. The experimental results on public datasets, ISCX VPNnon-VPN Traffic datasets, indicate that FDCNN architecture is practical and achieves higher accuracy. © Institute of Mathematical Statistics, 2022