A malicious attacker could, by taking control of internet-of-things devices, use them to capture received signal strength (RSS) measurements and perform surveillance on a person’s vital signs, activities, and sound in their environment. This article considers an attacker who looks for subtle changes in the RSS in order to eavesdrop sound vibrations. The challenge to the adversary is that sound vibrations cause very low amplitude changes in RSS, and RSS is typically quantized with a significantly larger step size. This article contributes a lower bound on an attacker’s monitoring performance as a function of the RSS step size and sampling frequency so that a designer can understand their relationship. Our bound considers the little-known and counter-intuitive fact that an adversary can improve their sinusoidal parameter estimates by making some devices transmit to add interference power into the RSS measurements. We demonstrate this capability experimentally. As we show, for typical transceivers, the RSS surveillance attacker can monitor sound vibrations with remarkable accuracy. New mitigation strategies will be required to prevent RSS surveillance attacks.

中文翻译：

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量化声音振动的干扰辅助信号强度监视

恶意攻击者可以通过控制物联网设备，使用它们来捕获接收信号强度（RSS）的测量值，并对环境中人员的生命体征，活动和声音进行监视。本文考虑了一个攻击者，他们寻找RSS中的细微变化以窃听声音振动。对手面临的挑战是声音振动会导致RSS的幅度变化非常小，并且RSS通常以较大的步长进行量化。本文根据RSS步长和采样频率，为攻击者的监视性能提供了一个下限，以便设计人员可以了解他们之间的关系。我们的界线考虑了鲜为人知且违反直觉的事实，即对手可以通过使某些设备进行发送以将干扰功率添加到RSS测量中来改善其正弦参数估计。我们通过实验证明了这种能力。正如我们所展示的，对于典型的收发器，RSS监视攻击者可以以惊人的精度监视声音振动。需要采取新的缓解策略来防止RSS监视攻击。