Network Traffic Classification (NTC) has become an important feature in
various network management operations, e.g., Quality of Service (QoS)
provisioning and security services. Machine Learning (ML) algorithms as a
popular approach for NTC can promise reasonable accuracy in classification and
deal with encrypted traffic. However, ML-based NTC techniques suffer from the
shortage of labeled traffic data which is the case in many real-world
applications. This study investigates the applicability of an active form of
ML, called Active Learning (AL), in NTC. AL reduces the need for a large number
of labeled examples by actively choosing the instances that should be labeled.
The study first provides an overview of NTC and its fundamental challenges
along with surveying the literature on ML-based NTC methods. Then, it
introduces the concepts of AL, discusses it in the context of NTC, and review
the literature in this field. Further, challenges and open issues in AL-based
classification of network traffic are discussed. Moreover, as a technical
survey, some experiments are conducted to show the broad applicability of AL in
NTC. The simulation results show that AL can achieve high accuracy with a small
amount of data.

本研究研究了主动学习在网络流量分类中的适用性，通过减少需要标记的示例数量来提高机器学习算法的分类准确性，结果表明，主动学习可以在处理较少数据时实现高准确性的网络流量分类。