Control flow graph modifications for improved RF-based processor tracking performance

Mark Chilenski; George Cybenko; Isaac Dekine; Piyush Kumar; Gil Raz

doi:10.1117/12.2316361

3 May 2018 Control flow graph modifications for improved RF-based processor tracking performance

Mark Chilenski, George Cybenko, Isaac Dekine, Piyush Kumar, Gil Raz

Proceedings Volume 10630, Cyber Sensing 2018; 106300I (2018) https://doi.org/10.1117/12.2316361
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Many dedicated embedded processors do not have memory or computational resources to coexist with traditional (host-based) security solutions. As a result, there is interest in using out-of-band analog side-channel measurements and their analyses to accurately monitor and analyze expected program execution. In this paper, we describe an approach to this problem using externally observable multi-band radio frequency (RF) measurements to make inferences about a program’s execution. Because it is very difficult to identify individual instructions solely from their RF emissions, we compare RF measurements with the constrained execution logic of the program so that multiple RF measurements over time can effectively track program execution dynamically. In our approach, a program’s execution is modeled by control flow graphs (CFG) and transitions between nodes of such graphs. We demonstrate that tracking performance can be improved through applications program modifications such as changing basic block transition properties and/or adding new basic blocks that are highly observable. In addition to demonstrating these principled approaches on some simple programs, we present initial results on the complexity and structure of real-world applications programs, namely gzip and md5sum, in this modeling framework.

Conference Presentation

Citation Download Citation

Mark Chilenski, George Cybenko, Isaac Dekine, Piyush Kumar, and Gil Raz "Control flow graph modifications for improved RF-based processor tracking performance", Proc. SPIE 10630, Cyber Sensing 2018, 106300I (3 May 2018); https://doi.org/10.1117/12.2316361

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