A Practical Methodology for ML-Based EM Side Channel Disassemblers release_eyph3jjp75hfhhonzy677733lq

by Cesar N. Arguello, Hunter Searle, Sara Rampazzi, Kevin R. B. Butler

Released as a article .

2022  

Abstract

Providing security guarantees for embedded devices with limited interface capabilities is an increasingly crucial task. Although these devices don't have traditional interfaces, they still generate unintentional electromagnetic signals that correlate with the instructions being executed. By collecting these traces using our methodology and leveraging a random forest algorithm to develop a machine learning model, we built an EM side channel based instruction level disassembler. The disassembler was tested on an Arduino UNO board, yielding an accuracy of 88.69% instruction recognition for traces from twelve instructions captured at a single location in the device; this is an improvement compared to the 75.6% (for twenty instructions) reported in previous similar work.
In text/plain format

Archived Content

There are no accessible files associated with this release. You could check other releases for this work for an accessible version.

"Dark" Preservation Only
Save Paper Now!

Know of a fulltext copy of on the public web? Submit a URL and we will archive it

Type  article
Stage   submitted
Date   2022-07-20
Version   v2
Language   en ?
arXiv  2206.10746v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: de4ecba2-d7dd-49b3-a27e-097a4ab7330e
API URL: JSON