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IEEE Signal Processing Magazine


The Internet of Things (IoT) and the Internet of Everything (IoE) have driven processors into nearly every powered de- vice, from thermostats to refrigerators to light bulbs. From a security perspective, the IoT and IoE create a new layer of sig- nals and systems that can provide insight into the internal opera- tions of a device via analog side channels. Our research focuses on leveraging these analog side channels in IoT/IoE processors to detect intrusions. Our goal is to defend against cyberattacks that insert malware into IoT devices by detecting deviations in the code running on their processors from known firmware. Our approach applies signal processing and machine-learning techniques to collected radio-frequency (RF) emissions of code running on the processor.

We describe our method of positioning a wide-bandwidth RF probe over the processor of the device under test (DuT) and then implementing classifiers to identify the code running on the device to detect, identify, and isolate register contents based on the signatures learned during the DuT characteriza- tion. The probe is placed to improve classifier discrimination between the instructions running on the DuT processor. At this selected location, the signatures of registers are subsequently extracted by separating the signals into execution components (i.e., instruction fetch and decode opcode). These techniques enable a significant reduction in feature dimensions, which improves the speed and accuracy of extracting the number of bit changes, also known as the Hamming distance (HD), in registers via the RF side channel. Using these signatures, we extract the HD of registers with a weighted average accuracy of greater than 99%.

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IEEE-INST Electrical Electronics Engineers INC



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