Active Learning for Network Traffic Classification: A Technical Study

TL;DR

This paper explores the use of Active Learning to improve network traffic classification, reducing labeled data requirements and maintaining high accuracy in machine learning-based approaches.

Contribution

It provides a comprehensive survey of AL in NTC, discusses challenges, and demonstrates through experiments that AL achieves high accuracy with less labeled data.

Findings

AL can significantly reduce labeled data needed for accurate NTC.

Experiments show high classification accuracy with limited labeled samples.

The study highlights open issues and future directions for AL in NTC.

Abstract

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.