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A Practical Methodology for ML-Based EM Side Channel Disassemblers
Cryptography and Security · Submitted 21 Jun 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Providing security guarantees for embedded devices with limited interface capabilities is an increasingly crucial task. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | Although these devices don't have traditional interfaces, they still generate unintentional electromagnetic signals that correlate with the instructions being executed. | background 1.00 | 0.10 | 0.02 | asserted 1.00 |
| s2 | 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. | method 0.83 | 0.96 | 0.18 | asserted 1.00 |
| s3 | 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. | result 1.00 | 0.89 | 0.68 | asserted 1.00 |
What was examined, and what was not
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The paper
Original 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.
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