Deep Learning for Encrypted Traffic Classification: An Overview

TL;DR

This paper reviews how deep learning techniques are transforming encrypted traffic classification, highlighting their advantages over traditional methods and discussing current challenges and future opportunities.

Contribution

It provides a comprehensive overview of deep learning frameworks and methods applied to traffic classification, including open problems and research opportunities.

Findings

Deep learning achieves higher accuracy in encrypted traffic classification.

Traditional methods decline in effectiveness with increased encryption.

The paper identifies key challenges and future directions in the field.

Abstract

Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in firewalls and intrusion detection systems. Port-based, data packet inspection, and classical machine learning methods have been used extensively in the past, but their accuracy have been declined due to the dramatic changes in the Internet traffic, particularly the increase in encrypted traffic. With the proliferation of deep learning methods, researchers have recently investigated these methods for traffic classification task and reported high accuracy. In this article, we introduce a general framework for deep-learning-based traffic classification. We present commonly used deep learning methods and their application in traffic classification tasks. Then, we discuss open problems and their challenges, as well as opportunities for traffic classification.