Year: 2025
Type: article
Abstract: This paper presents an automated two-stage framework for detecting and classifying electromagnetic emissions produced during the execution of block ciphers on microcontrollers. The proposed method integrates a one-dimensional convolutional autoencoder for unsupervised anomaly detection with a one-dimensional convolutional neural network classifier for algorithm identification. Real EM traces were collected from STM32L476RG and RP2040 microcontrollers executing AES, DES, 3DES, SM4, IDEA, and Blowfish in ECB mode. The autoencoder achieves reliable segmentation of cryptographic activity without external triggers, while the CNN classifies extracted windows with an average accuracy of 93% across seven classes. The method demonstrates strong robustness to noise, generalizes across devices, and enables non-invasive monitoring of cryptographic activity.
Source: Applied Sciences
DOI: 10.3390/app152412935
Authors: Konrad Szczepankiewicz, Marian Wnuk
Institution: Military University of Technology in Warsaw
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4417121828