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The penetration of embedded devices in networks that support criticalapplications has rendered them a lucrative target for attackers and evildoers.However, traditional protection mechanisms may not be supported due to thememory and computational limitations of these systems. Recently, the analysisof electromagnetic (EM) emanations has gathered the interest of the researchcommunity. Thus, analogous protection systems have emerged as a viable solutione.g., for providing external, non-intrusive control-flow attestation forresource-constrained devices. Unfortunately, the majority of current work failsto account for the implications of real-life factors, predominantly the impactof environmental noise. In this work, we introduce a framework that integratessingular value decomposition (SVD) along with outlier detection for discoveringmalicious modifications of embedded software even under variable conditions ofnoise. Our proposed framework achieves high detection accuracy i.e., above 93\%AUC score for unknown attacks, even for extreme noise conditions i.e., -10 SNR.To the best of our knowledge, this is the first time this realistic limitingfactor, i.e., environmental noise, is successfully addressed in the context ofEM-based anomaly detection for embedded devices.

Detecting Code Injections in Noisy Environments Through EM Signal Analysis and SVD Denoising