The use of involuntary analog side-channel emissions to remotely identify the internal state of digital platforms hasrecently emerged as a valuable tool in the arsenal of defensive measures against intrusion and malicious attacks,as well as hardware modi cations. In particular RF emissions have been shown to be e ective in this task. Oneof the key challenges is identifying and selecting useful features from the noisy signals which simultaneouslyenable the detection of the internal digital state reliably while minimizing the complexity of this operation. Ourteam has developed such sensors and we show the ability to optimally select features as well as optimally selectbands of operation from which features can be drawn. Optimality here is in the sense of maximizing the mutualinformation between the features and the true state of the devices under test. In addition to being optimal in thesense of performance and low complexity for the real-time operation, the process of nding the optimal featuresis parsimonious and amenable to deployment in adaptive real-time sensors. In these proceedings we describespeci c examples related to the detection of intended vs unintended programs on IoT devices and FPGAs aswell as identi cation of other internal device settings. We show near-perfect identi cation of such internal states,achieved in real-time at distances of several feet in challenging environments.
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