  Class-of-service (CoS) network traffic classification (NTC) classifies a
group of similar traffic applications. The CoS classification is advantageous
in resource scheduling for Internet service providers and avoids the necessity
of remodelling. Our goal is to find a robust, lightweight, and fast-converging
CoS classifier that uses fewer data in modelling and does not require
specialized tools in feature extraction. The commonality of statistical
features among the network flow segments motivates us to propose novel
segmented learning that includes essential vector representation and a
simple-segment method of classification. We represent the segmented traffic in
the vector form using the EVR. Then, the segmented traffic is modelled for
classification using random forest. Our solution's success relies on finding
the optimal segment size and a minimum number of segments required in
modelling. The solution is validated on multiple datasets for various CoS
services, including virtual reality (VR). Significant findings of the research
work are i) Synchronous services that require acknowledgment and request to
continue communication are classified with 99% accuracy, ii) Initial 1,000
packets in any session are good enough to model a CoS traffic for promising
results, and we therefore can quickly deploy a CoS classifier, and iii) Test
results remain consistent even when trained on one dataset and tested on a
different dataset. In summary, our solution is the first to propose
segmentation learning NTC that uses fewer features to classify most CoS traffic
with an accuracy of 99%. The implementation of our solution is available on
GitHub.
