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Proceedings Paper

Instruction level program tracking using electromagnetic emanations
Author(s): Baki Berkay Yilamz; Elvan Mert Ugurlu; Alenka Zajic; Milos Prvulovic
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Paper Abstract

Monitoring computer system activities on the instruction level provides more resilience to malware attacks because these attacks can be analyzed better by observing the changes on the instruction level. Assuming the source code is available, many training signals can be collected to track the instruction sequence to detect whether a malware is injected or the system works properly. However, training signals have to be collected with high sampling rate to ensure that the significant features of these signals do not vanish. Since the clock frequencies of the current computer systems are extremely high, we need to have a commercial device with high sampling rate, i.e. 10GHz, which either costs remarkably high, or does not exist. To eliminate the deficiencies regarding the insufficient sampling rate, we propose a method to increase the sampling rate with the moderate commercial devices for training symbols. In that respect, we first generate some random instruction sequences which exist in the inspected source code. Then, these sequences are executed in a for-loop, and emanated electromagnetic (EM) signals from the processor are collected by a commercially available device with moderate sampling rate, i.e. sampling rate is much smaller than the clock frequency. Lastly, we apply a mapping of the gathered samples by utilizing modulo of their timings with respect to execution time of overall instruction sequence. As the final step, we provide some experimental results to illustrate that we successfully track the instruction sequence by applying the proposed approach.

Paper Details

Date Published: 17 May 2019
PDF: 12 pages
Proc. SPIE 11011, Cyber Sensing 2019, 110110H (17 May 2019); doi: 10.1117/12.2522620
Show Author Affiliations
Baki Berkay Yilamz, Georgia Institute of Technology (United States)
Elvan Mert Ugurlu, Georgia Institute of Technology (United States)
Alenka Zajic, Georgia Institute of Technology (United States)
Milos Prvulovic, Georgia Institute of Technology (United States)

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

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