Detail výsledku
Multichannel Histograms for Flow Classification
Jeřábek Kamil, Ing., Ph.D., UIFS (FIT)
Kolář Dušan, doc. Dr. Ing., UIFS (FIT)
Čejka Tomáš, doc. Ing., Ph.D.
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.
Computer Network Monitoring, Traffic Classification, Neural Networks, Histograms
@inproceedings{BUT193582,
author="Daniel {Poliakov} and Kamil {Jeřábek} and Dušan {Kolář} and Tomáš {Čejka}",
title="Multichannel Histograms for Flow Classification",
booktitle="38th IEEE/IFIP Network Operations and Management Symposium (NOMS 2025)",
year="2025",
pages="5",
publisher="IEEE Communications Society",
address="Honolulu",
doi="10.1109/NOMS57970.2025.11073699",
isbn="9798331531638"
}