Identification of Microcontroller Unit Instruction Execution Using Electromagnetic Leakage and Neural Network Classification

In this article, a novel method is proposed for determining the running state of a system through the classification of electromagnetic interference (EMI) leakage using neural network (NN) models. A modified IEC 61967 measurement platform is used to analyze the EMI signals of a microcontroller unit...

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Bibliographic Details
Published inIEEE transactions on electromagnetic compatibility Vol. 64; no. 4; pp. 930 - 940
Main Author Yuan, Shih-Yi
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Analytical models
Classification
Data models
Electromagnetic interference
Electromagnetic interference (EMI)
Frequency-domain analysis
IEC 61967
Leakage
Microcontrollers
neural network (NN)
Neural networks
Solid modeling
Time-domain analysis
Training
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Summary:In this article, a novel method is proposed for determining the running state of a system through the classification of electromagnetic interference (EMI) leakage using neural network (NN) models. A modified IEC 61967 measurement platform is used to analyze the EMI signals of a microcontroller unit during its operation. A total of 17 NN models are developed and tested to determine the optimal model. The optimal NN model has ungrouped-Top3 and ungrouped-Top5 accuracies of 77.13% and 91.94%, respectively. The ungrouped-Top1 accuracy is improved by 7.53%. They are the highest improvements ever achieved to the best of the author's knowledge.
ISSN:0018-9375
1558-187X
DOI:10.1109/TEMC.2022.3159868