Multichannel Histograms for Flow Classification

Encrypted traffic poses increasing challenges for effective network monitoring and management. To address this, machine learning and deep learning techniques are commonly applied to encrypted traffic classification by analyzing the statistical properties of network flows, such as packet lengths and inter-arrival times. Rather than relying on point estimates of these properties, we aim to obtain a comprehensive view of the distribution using histograms. In this paper, we propose and analyze a multichannel 2D histogram representation that incorporates packet lengths, directions, and inter-arrival times, extending the prior well-performing flowpic representation. We classify these histograms using a 2D convolutional neural network with residual connections, improving classification accuracy over previous methods.