Flow-Packet Hybrid Traffic Classification for Class-Aware Network Routing
Sayantan Chowdhury, Ben Liang, Ali Tizghadam, Ilijc Albanese

TL;DR
This paper proposes a flow-packet hybrid traffic classification method that enables real-time, efficient, and accurate packet-level decisions for network routing by transferring knowledge from flow-based classifiers.
Contribution
It introduces FPHTC, a novel hybrid classification approach that combines flow-based learning with packet-level decision making suitable for routers.
Findings
FPHTC is robust to traffic pattern changes.
It achieves accurate classification with limited computational resources.
It outperforms traditional packet-based classifiers in generalization bound.
Abstract
Network traffic classification using machine learning techniques has been widely studied. Most existing schemes classify entire traffic flows, but there are major limitations to their practicality. At a network router, the packets need to be processed with minimum delay, so the classifier cannot wait until the end of the flow to make a decision. Furthermore, a complicated machine learning algorithm can be too computationally expensive to implement inside the router. In this paper, we introduce flow-packet hybrid traffic classification (FPHTC), where the router makes a decision per packet based on a routing policy that is designed through transferring the learned knowledge from a flow-based classifier residing outside the router. We analyze the generalization bound of FPHTC and show its advantage over regular packet-based traffic classification. We present experimental results using a…
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