Screen Gleaning：利用电磁侧信道对移动设备进行屏幕读取TEMPEST攻击
Screen Gleaning: A Screen Reading TEMPEST Attack on Mobile Devices Exploiting an Electromagnetic Side Channel
摘要：
我们介绍屏幕搜集，TEMPEST攻击，其中移动终端的屏幕被读取没有视觉视线，揭示手机屏幕上显示的敏感信息。屏幕收集攻击使用天线和软件定义无线电（SDR）来拾取设备发送到屏幕上显示的电磁信号，例如，一条带有安全密码的信息这种特殊的设备可以将信号重建为灰度图像，我们称之为图像。在这里，我们展示了它可以用来读取安全代码。屏幕收集攻击是具有挑战性的，因为人类观众通常不可能直接解释消息。我们证明了这个挑战可以通过机器学习来解决，特别是深度学习分类器。随着SDR和深度学习继续快速发展，屏幕收集将变得越来越严重。在本文中，我们展示了安全代码攻击，我们提出了一个测试平台，提供了一个标准的设置，其中屏幕搜集可以测试不同的攻击模型。最后，我们分析了屏幕收集攻击者模型的维度，并讨论了可能的对策，以解决这些问题。
英文摘要：
We introduce screen gleaning, a TEMPEST attack in which the screen of a mobile device is read without a visual line of sight, revealing sensitive information displayed on the phone screen. The screen gleaning attack uses an antenna and a software-defined radio (SDR) to pick up the electromagnetic signal that the device sends to the screen to display, e.g., a message with a security code. This special equipment makes it possible to recreate the signal as a gray-scale image, which we refer to as an emage. Here, we show that it can be used to read a security code. The screen gleaning attack is challenging because it is often impossible for a human viewer to interpret the emage directly. We show that this challenge can be addressed with machine learning, specifically, a deep learning classifier. Screen gleaning will become increasingly serious as SDRs and deep learning continue to rapidly advance. In this paper, we demonstrate the security code attack and we propose a testbed that provides a standard setup in which screen gleaning could be tested with different attacker models. Finally, we analyze the dimensions of screen gleaning attacker models and discuss possible countermeasures with the potential to address them.
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