Traffic classification acquired the interest of the Internet community early on. Different approaches have been proposed to classify Internet traffic to manage both security and Quality of Service (QoS). However, traditional classification approaches consisting of modifying the Transmission Control Protocol/Internet Protocol (TCP/IP) scheme have not been adopted due to their complex management. In addition, port-based methods and deep packet inspection have limitations in dealing with new traffic characteristics (e.g., dynamic port allocation, tunneling, encryption). Conversely, machine learning (ML) solutions effectively classify traffic down to the device type and specific user action. Another research direction aims to anonymize Internet traffic and thwart classification to maintain user privacy. Existing traffic surveys focus on classification and do not consider anonymization. Here, we review the Internet traffic classification and obfuscation techniques, largely considering the ML-based solutions. In addition, this paper presents a comprehensive review of various data representation methods, and the different objectives of Internet traffic classification. Finally, we present the key findings, limitations, and recommendations for future research.

流量分类很早就引起了Internet社区的关注。已经提出了不同的方法来对互联网流量进行分类，以管理安全性和服务质量（QoS）。但是，由于管理复杂，尚未采用包括修改传输控制协议/ Internet协议（TCP / IP）方案的传统分类方法。另外，基于端口的方法和深度数据包检查在处理新的流量特征（例如，动态端口分配，隧道传输，加密）方面存在局限性。相反，机器学习（ML）解决方案有效地将流量分类为设备类型和特定用户操作。另一个研究方向旨在匿名化Internet流量和阻止分类以维护用户隐私。现有的流量调查只关注分类，而不考虑匿名化。在这里，我们主要考虑基于ML的解决方案，回顾Internet流量分类和混淆技术。此外，本文对各种数据表示方法以及Internet流量分类的不同目标进行了全面回顾。最后，我们介绍了关键的发现，局限性和对未来研究的建议。