Deep Learning for Minimal-context Block Tracking through Side-channel Analysis
L. Jensen, G. Brown, Xiao Wang, Jacob Harer, S. Chin
Abstract
It is well known that electromagnetic and power side-channel attacks allow extraction of unintended information from a computer processor. However, little work has been done to quantify how small a sample is needed in order to glean meaningful information about a program’s execution. This paper quantifies this minimum context by training a deep-learning model to track and classify program block types given small windows of side-channel data. We show that a window containing approximately four clock cycles suffices to predict block type with our experimental setup. This implies a high degree of information leakage through side channels, allowing for the external monitoring of embedded systems and Internet of Things devices.
BibTeX
@inproceedings{icassp2019_deeplearningform,
title = {Deep Learning for Minimal-context Block Tracking through Side-channel Analysis},
author = {L. Jensen and G. Brown and Xiao Wang and Jacob Harer and S. Chin},
booktitle = {ICASSP 2019},
year = {2019}
}