| Autor: |
DANIAL, JOSEF, DAS, DEBAYAN, GOLDER, ANUPAM, GHOSH, SANTOSH, RAYCHOWDHURY, ARIJIT, SEN, SHREYAS |
| Předmět: |
|
| Zdroj: |
ACM Journal on Emerging Technologies in Computing Systems; Jan2022, Vol. 18 Issue 1, p1-17, 17p |
| Abstrakt: |
This work presents a Cross-device Deep-Learning based Electromagnetic (EM-X-DL) side-channel analysis (SCA) on AES-128, in the presence of a significantly lower signal-to-noise ratio (SNR) compared to previous works. Using a novel algorithm to intelligently select multiple training devices and proper choice of hyperparameters, the proposed 256-class deep neural network (DNN) can be trained efficiently utilizing pre-processing techniques like PCA, LDA, and FFT on measurements from the target encryption engine running on an 8-bit Atmel microcontroller. In this way, EM-X-DL achieves >90% single-trace attack accuracy. Finally, an efficient end-to-end SCA leakage detection and attack framework using EM-X-DL demonstrates high confidence of an attacker with <20 averaged EM traces. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
