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Anomalous activity detection using RF emanations
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AI
DOI：10.1016/j.sigpro.2025.110201.png)
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Abstract
En 中文
Electronic activity in digital systems unintentionally emits radio frequency (RF) signals called emanations. These emanations compromise data security, which is important for corporate and military establishments. This work focuses on detecting anomalous activity that compromises data security through emanations. An example of such anomalous activity is emanations from damaged peripherals, such as a mouse or keyboard, which can be used to steal digital data. Prior work on emanation detection uses profiling on specific hardware (HW). However, this is not scalable across all types of HW. We propose a HW-agnostic solution for finding anomalous activity using emanations by scanning the signature of harmonics from leakages of clock signals. An algorithm for multi-harmonic pitch estimation is introduced for wireless applications. A preprocessing technique is developed that removes the effect of artifacts. Thorough mathematical derivations demonstrate the algorithm theoretically. In-phase and Quadrature-phase (IQ) data are collected from emanation sources placed in a shielded room from 0.1–1.1 GHz using software-defined radios (SDR). Results are presented for use cases emulating anomalous activity that compromises data security, such as damaged peripherals and unauthorized data copy onto external devices.
Keywords:
Emanations
Side-channel attacks
RF anomaly
Data security and privacy
Unintended RF emission
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