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Proceedings Paper

Asynchronous data collection and classification for RF side-channel analysis
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Paper Abstract

Side-channel analysis (SCA) provides an independent, non-invasive remote monitoring solution to determine the digital state of a programmable electronic device. In our work, we have conducted near-field SCA on various devices to determine how well different programs running on devices can be differentiated. We have tested devices ranging from the relatively simple Arduino Uno to the much more complex Samsung Galaxy S8. The antennas used for radio frequency (RF) collection have also varied from the self-contained ~500MHz Riscure probe to a 40mm Triarchy Loop antenna with attached amplifier. Our study implemented various collection techniques; however, all of them relied on the constraint of a trigger signal. The trigger signal was needed to initiate the data collection process and to act as a reference for sequencing the various blocks within a code execution. However, a trigger signal is not always available or even feasible to obtain from a device for remote monitoring applications. This work investigates potential methods for triggerless detection and alignment of digital code blocks on measured analog RF data. Methods for performing the detection range from boosting codes that generate easily aligned RF pulses, to correlation methods for signal alignment. The varying quality of RF data generated between the devices and the amount of noise embedded in the signals from the measurement schemes negatively impact triggerless collection. We estimate our probability of success at aligning signals to exceed 90% for the devices tested.

Paper Details

Date Published: 17 May 2019
PDF: 7 pages
Proc. SPIE 11011, Cyber Sensing 2019, 110110D (17 May 2019); doi: 10.1117/12.2518712
Show Author Affiliations
James Graham, Riverside Research (United States)
Samuel V. Mantravadi, Riverside Research (United States)
Ashwin Fisher, Riverside Research (United States)

Published in SPIE Proceedings Vol. 11011:
Cyber Sensing 2019
Igor V. Ternovskiy; Peter Chin, Editor(s)

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