Deep Learning for Encrypted Traffic Classification: An Overview
Deep Learning for Encrypted Traffic Classification: An Overview
复制标题
DOI：
10.1109/mcom.2019.1800819
复制
发表时间：
2019-05-01
影响因子：
11.2
通讯作者：
Liu, Xin
中科院分区：
文献类型：
--
作者：
Rezaei, Shahbaz;Liu, Xin
流量分类已经被研究了二十年，并应用于广泛的应用，从isp的QoS提供和计费到防火墙和入侵检测系统中的安全相关应用。基于端口、数据包检测和经典机器学习方法在过去被广泛使用，但由于互联网流量的急剧变化，特别是加密流量的增加，它们的准确性下降了。随着深度学习方法的普及，研究人员最近对这些方法进行了研究，并报道了较高的准确率。在本文中，我们介绍了一个基于深度学习的流量分类的通用框架。我们介绍了常用的深度学习方法及其在流量分类任务中的应用。然后讨论流量分类的开放性问题、挑战和机遇。
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 has declined due to the dramatic changes in Internet traffic, particularly the increase in encrypted traffic. With the proliferation of deep learning methods, researchers have recently investigated these methods for traffic classification 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, challenges, and opportunities for traffic classification.