According to the critical requirements of the Internet, a wide range of privacy-preserving technologies are available, e.g. proxy sites, virtual private networks, and anonymity tools. Such mechanisms are challenged by traffic-classification endeavors which are crucial for network-management tasks and have recently become a milestone in their privacy-degree assessment, both from attacker and designer standpoints. Further, the new Internet era is characterized by the capillary distribution of smart devices leveraging high-capacity communication infrastructures: this results in huge amount of heterogeneous network traffic, i.e. big data. Hence, herein we present BDeH, a novel hierarchical framework for traffic classification of anonymity tools. BDeH is enabled by big data-paradigm and capitalizes the machine learning workhorse for operating with encrypted traffic. In detail, our proposal allows for seamless integration of data parallelism provided by big-data technologies with model parallelism enabled by hierarchical approaches. Results prove that the so-achieved double parallelism carries no negative impact on traffic-classification effectiveness at any granularity level and achieves non negligible performance enhancements with respect to non-hierarchical architectures ($+4.5\%$ F-measure). Also, it significantly gains over either pure data or pure model parallelism (resp. centralized) approaches by reducing both training completion time—up to $78\%$ (resp. $90\%$)—and cloud-deployment cost—up to $31\%$ (resp. $10\%$).

中文翻译：

---

一种基于大数据的流量分类分层框架

根据互联网的关键要求，可以使用范围广泛的隐私保护技术，例如代理站点、虚拟专用网络和匿名工具。这种机制受到流量分类努力的挑战，流量分类对网络管理任务至关重要，最近已成为其隐私程度评估的里程碑，无论是从攻击者还是设计者的角度来看。此外，新互联网时代的特点是利用大容量通信基础设施的智能设备的毛细分布：这导致了大量的异构网络流量，即大数据。因此，我们在此介绍 BDeH，这是一种用于匿名工具流量分类的新型分层框架。BDeH 由大数据范式支持，并利用机器学习的主力来处理加密流量。详细地说，我们的提议允许无缝集成数据并行 由大数据技术提供 模型并行通过分层方法启用。结果证明达到了双重并行 对任何粒度级别的流量分类有效性都没有负面影响，并且相对于非分层架构实现了不可忽视的性能增强（$+4.5\%$F-测量）。此外，通过减少训练完成时间，它显着优于纯数据或纯模型并行（或集中式）方法 - 最多$78\%$ （分别 $90\%$)——以及云部署成本——高达 $31\%$ （分别 $10\%$）。