Virtual reality (VR), offering 3D visuals and stereophonic sounds, significantly enhances users’ immersive experiences and has become a milestone in the era of the metaverse. However, due to the limited battery capacity of VR devices, it is common for users to rely on charging cables, which serve the dual purpose of power supply and audio output, to recharge their VR devices while in use. In this study, we propose an inconspicuous and stealthy side channel attack, coined as LineTalker, which can unveil visual-related and audio-related activities from VR devices during the charging process. The insight behind LineTalker is rooted in the observation that visual-related activities (e.g., 3D image rendering) are power-intensive and result in fluctuations in the current strength of the cable’s power supply line, which can be leveraged as side channel information. Similarly, audio-related activities (e.g., playing music) leave traces on the cable’s audio output line. Rather than providing a user with a compromised charging cable (i.e., embedding a current sensor) to measure the current strength, to make the attack less conspicuous, LineTalker employs the Hall effect to indirectly access side channel information. This is achieved by capturing magnetic signals using a Hall sensor placed near the target cable in a contactless manner. Experimental results demonstrate that LineTalker achieves an overall accuracy of 94.60% and 64.38% in inferring user activities in VR devices with intrusive and non-intrusive attack manners, respectively.

中文翻译：

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为 VR 设备充电的危险：通过充电线进行隐藏的侧信道攻击


虚拟现实 （VR） 提供 3D 视觉效果和立体声，显着增强了用户的沉浸式体验，已成为元宇宙时代的里程碑。然而，由于 VR 设备的电池容量有限，用户在使用 VR 设备时通常依靠充电电缆（具有电源和音频输出的双重用途）为其 VR 设备充电。在这项研究中，我们提出了一种不显眼且隐蔽的侧信道攻击，称为 LineTalker，它可以在充电过程中揭示 VR 设备与视觉和音频相关的活动。LineTalker 背后的洞察力植根于这样一种观察，即与视觉相关的活动（例如，3D 图像渲染）是功率密集型的，并导致电缆电源线电流强度的波动，这可以用作侧信道信息。同样，与音频相关的活动（例如播放音乐）会在电缆的音频输出线上留下痕迹。LineTalker 没有为用户提供受损的充电电缆（即嵌入电流传感器）来测量电流强度，以减少攻击的明显性，而是利用霍尔效应间接访问侧信道信息。这是通过使用放置在目标电缆附近的霍尔传感器以非接触方式捕获磁信号来实现的。实验结果表明，LineTalker 在具有侵入和非侵入式攻击方式的 VR 设备中推断用户活动时，总体准确率分别为 94.60% 和 64.38%。