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Information theoretical optimal use of RF side channels for microsystem characterization
Author(s): Isaac Dekine; Mark Chilenski; Corey Cleveland; Piyush Kumar; Gil Raz; Max Li
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Paper Abstract

The use of involuntary analog side-channel emissions to remotely identify the internal state of digital platforms has recently emerged as a valuable tool in the arsenal of defensive measures against intrusion and malicious attacks, as well as hardware modifications. In particular RF emissions have been shown to be effective in this task. One of the key challenges is identifying and selecting useful features from the noisy signals which simultaneously enable the detection of the internal digital state reliably while minimizing the complexity of this operation. Our team has developed such sensors and we show the ability to optimally select features as well as optimally select bands of operation from which features can be drawn. Optimality here is in the sense of maximizing the mutual information between the features and the true state of the devices under test. In addition to being optimal in the sense of performance and low complexity for the real-time operation, the process of finding the optimal features is parsimonious and amenable to deployment in adaptive real-time sensors. In these proceedings we describe specific examples related to the detection of intended vs unintended programs on IoT devices and FPGAs as well as identification of other internal device settings. We show near-perfect identification of such internal states, achieved in real-time at distances of several feet in challenging environments.

Paper Details

Date Published: 17 May 2019
PDF: 13 pages
Proc. SPIE 11011, Cyber Sensing 2019, 1101108 (17 May 2019); doi: 10.1117/12.2526358
Show Author Affiliations
Isaac Dekine, Systems & Technology Research (United States)
Mark Chilenski, Systems & Technology Research (United States)
Corey Cleveland, Systems & Technology Research (United States)
Piyush Kumar, Systems & Technology Research (United States)
Gil Raz, Systems & Technology Research (United States)
Max Li, Systems & Technology Research (United States)


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

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