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 present a general framework for deep-learningbased traffic classification. We discuss commonly used deep learning methods and their application in traffic classification tasks. Then, we present open problems and their challenges, as well as opportunities for traffic classification.
Machine learning techniques have even been used to classify encrypted traffic REF .
Deep Learning for Encrypted Traffic Classification: An Overview
IEEE Communications Magazine 57 (2019) 76-81
Computer Science