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USB side-channel attack on Tor

机译:USB对Tor的侧通道攻击

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摘要

Tor is used to communicate anonymously by millions of daily users, which rely on it for their privacy, security, and often safety. In this paper we present a new attack on Tor that allows a malicious USB charging device (e.g., a public USB charging station) to identify which website is being visited by a smartphone user via Tor, thus breaking Tor's primary use case. Our attack solely depends on power measurements performed while the user is charging her smartphone, and it does not require the adversary to observe any network traffic or to transfer data through the smartphone's USB port. We evaluated the attack by training a machine learning model on power traces from 50 regular webpages and 50 Tor hidden services. We considered realistic constraints such as different network types (LTE and WiFi), Tor circuit types, and battery charging levels. In our experiments, we were able to correctly identify webpages visited using the official mobile Tor browser with accuracies up to 85.7% when the battery was fully charged, and up to 46% when the battery level was between 30% and 50%. Both results are substantially higher than the 1% baseline of random guessing. Surprisingly, our results show that hidden services can be identified with higher accuracies than regular webpages (e.g., 84.3% vs. 68.7% over LTE). (C) 2018 Elsevier B.V. All rights reserved.
机译:Tor用于数以百万计的日常用户进行匿名通信,这些用户依赖于Tor的隐私,安全性以及通常的安全性。在本文中,我们提出了一种针对Tor的新攻击,该攻击允许恶意USB充电设备(例如,公共USB充电站)通过Tor识别智能手机用户正在访问哪个网站,从而打破了Tor的主要用例。我们的攻击完全取决于用户为智能手机充电时执行的功率测量,并且不需要对手观察任何网络流量或通过智能手机的USB端口传输数据。我们通过在50个常规网页和50个Tor隐藏服务的电源迹线上训练机器学习模型来评估攻击。我们考虑了现实的约束,例如不同的网络类型(LTE和WiFi),Tor电路类型和电池充电水平。在我们的实验中,我们能够正确识别使用官方移动Tor浏览器访问的网页,当电池充满电时,其准确率高达85.7%,而当电池电量在30%至50%之间时,准确率高达46%。两种结果都大大高于随机猜测的1%基线。令人惊讶的是,我们的结果表明,与常规网页相比,隐藏服务的识别准确度更高(例如,LTE中为84.3%,而68.7%)。 (C)2018 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Computer networks》 |2018年第4期|57-66|共10页
  • 作者单位

    Coll William & Mary, Dept Comp Sci, Williamsburg, VA 23185 USA;

    New York Inst Technol, Sch Engn & Comp Sci, New York, NY USA;

    New York Inst Technol, Sch Engn & Comp Sci, New York, NY USA;

    Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China;

    Coll William & Mary, Dept Comp Sci, Williamsburg, VA 23185 USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Tor; Side-channel attacks; De-anonymization; Privacy;

    机译:Tor;侧通道攻击;去匿名化;隐私权;

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