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The globalization of electronic systems’ fabrication has made some of our most critical systems vulnerable to supply chain attacks. Implanting spy chips on the printed circuit boards (PCBs) or replacing genuine components with counterfeit/recycled ones are examples of such attacks. Unfortunately, conventional attack detection schemes for PCBs are ad-hoc, costly, unscalable, and error-prone. This work introduces a holistic physical verification framework for PCBs, called ScatterVerif , based on the characterization of the PCBs’ power distribution network (PDN). First, we demonstrate how scattering parameters, frequently used for impedance characterization of RF circuits, can characterize the entire PCB with a single measurement. Second, we present how a class of machine learning algorithms, namely the Gaussian Mixture Model, can be applied to the measurements to automatically classify/cluster the genuine and tampered/counterfeit PCBs. We show that these attacks affect the overall impedance of a PCB differently in various frequency ranges , and hence, the conventional impedance measurements using a constant-frequency electrical stimulus might leave the attack undetected. We conduct extensive experiments on counterfeit and tampered devices and demonstrate that these attacks can be detected with a high-confidence. Finally, we show that the acquired data from the PDN characterization can also be deployed for fingerprinting genuine PCBs.

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