Poster: Data Collection for ML Classification of Encrypted Messaging Applications

Jason Hussey, Ethan Taylor, Kerri Stone, T. Camp
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Abstract

Network traffic classification is used to identify the nature of traffic on a network. Entities capable of monitoring net-work traffic use classification for all manner of reasons, including identification of mobile applications being used on the network. It is possible that the usage of encrypted messaging applications by users on these networks can be detected, betraying elements of their privacy.In this paper, we describe a system that leverages campus network resources to generate real-world data alongside a more curated dataset captured from Android application traffic. We also explore the ability of machine learning (ML) models to accurately classify traffic from these encrypted messaging applications. Understanding what is revealed from network data is important given that the use of these applications is meant to maximize privacy in the first place.
海报:加密消息应用的ML分类数据收集
网络流分类用于识别网络中流量的性质。能够监控网络流量的实体出于各种原因使用分类,包括识别网络上使用的移动应用程序。用户在这些网络上使用的加密消息传递应用程序有可能被检测到,从而泄露了他们的隐私。在本文中,我们描述了一个利用校园网资源生成真实世界数据的系统,以及从Android应用程序流量中捕获的更精心策划的数据集。我们还探索了机器学习(ML)模型准确分类来自这些加密消息传递应用程序的流量的能力。考虑到使用这些应用程序首先是为了最大限度地保护隐私,了解从网络数据中泄露的内容非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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