FingerprinTV: Fingerprinting Smart TV Apps
FingerprinTV: Fingerprinting Smart TV Apps
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DOI：
10.56553/popets-2022-0088
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发表时间：
2022-07
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影响因子：
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通讯作者：
Janus Varmarken;Jad Al Aaraj;R. Trimananda;A. Markopoulou
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文献类型：
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作者：
Janus Varmarken;Jad Al Aaraj;R. Trimananda;A. Markopoulou
本文提出了 FingerprinTV，这是一种完全自动化的方法，用于从智能电视应用程序的网络流量中提取指纹并评估其性能。 FingerprintTV (1) 安装、重复启动并收集智能电视应用程序的网络流量； （2）为每个应用程序提取三种不同类型的网络指纹，即基于域的指纹（DBF）、基于数据包对的指纹（PBF）和基于TLS的指纹（TBF）； (3) 分析提取的指纹的普遍性、独特性和大小。通过将FingerprinTV应用于三个最流行的智能电视平台的前1000个应用程序，我们发现智能电视应用程序网络指纹识别是可行和有效的：即使是最不流行的指纹类型也至少在每个平台的68％的应用程序中出现，并且当两种指纹识别技术一起使用时，高达89％的指纹可以唯一地识别特定的应用程序。通过分析具有相同指纹的应用程序，我们发现这些应用程序通常源自同一开发人员或“无代码”应用程序生成工具包。此外，我们还表明，所有三个平台上都存在的许多应用程序都表现出特定于平台的指纹。
This paper proposes FingerprinTV, a fully automated methodology for extracting fingerprints from the network traffic of smart TV apps and assessing their performance. FingerprinTV (1) installs, repeatedly launches, and collects network traffic from smart TV apps; (2) extracts three different types of network fingerprints for each app, i.e., domain-based fingerprints (DBF), packet-pair-based fingerprints (PBF), and TLS-based fingerprints (TBF); and (3) analyzes the extracted fingerprints in terms of their prevalence, distinctiveness, and sizes. From applying FingerprinTV to the top-1000 apps of the three most popular smart TV platforms, we find that smart TV app network fingerprinting is feasible and effective: even the least prevalent type of fingerprint manifests itself in at least 68% of apps of each platform, and up to 89% of fingerprints uniquely identify a specific app when two fingerprinting techniques are used together. By analyzing apps that exhibit identical fingerprints, we find that these apps often stem from the same developer or “no code” app generation toolkit. Furthermore, we show that many apps that are present on all three platforms exhibit platformspecific fingerprints.