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Detecting repeating software operations in code blocks using RF side-channel analysis
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Paper Abstract

Side-channel analysis covers several methods for determining the state of a device without directly interacting with the device. In previous work, we collected near-field radio frequency emanations from simple programs to assess how various code operations could be differentiated at the instruction level. However, detecting operations in large blocks of instructions in more complicated programs have proven difficult due to the high dimensionality of the data. In this research, we examine methods to differentiate common operations using RF emanations. We use a series of example codes useful for Two Factor Authentication on an Arduino Mega. Some examples are coded with extra operations to simulate malware such as intentionally leaking the key, nuisance operations, or substituting a weaker hash function. After collecting RF data, approximation techniques are used to reduce the data dimensionality and identify motifs in the time series. The motifs are correlated with the operations taking place by use of a uniquely identifiable triggering mechanism. Several exemplary motifs are then used together as templates that can be used to search for a connected series of operations. These templates are compared with an RF time series of unknown operations using a minimum distance metric. We evaluate the quality of templates available from an RF data collection and examine the usefulness of templates as features for classification.

Paper Details

Date Published: 17 May 2019
PDF: 9 pages
Proc. SPIE 11011, Cyber Sensing 2019, 1101109 (17 May 2019); doi: 10.1117/12.2524124
Show Author Affiliations
Samuel V. Mantravadi, Riverside Research (United States)
James T. Graham, 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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