Paper 2019/818
X-DeepSCA: Cross-Device Deep Learning Side Channel Attack
Debayan Das, Anupam Golder, Josef Danial, Santosh Ghosh, Arijit Raychowdhury, and Shreyas Sen
Abstract
This article, for the first time, demonstrates Cross-device Deep Learning Side-Channel Attack (X-DeepSCA), achieving an accuracy of $>99.9\%$, even in presence of significantly higher inter-device variations compared to the inter-key variations. Augmenting traces captured from multiple devices for training and with proper choice of hyper-parameters, the proposed 256-class Deep Neural Network (DNN) learns accurately from the power side-channel leakage of an AES-128 target encryption engine, and an N-trace ($N\leq10$) X-DeepSCA attack breaks different target devices within seconds compared to a few minutes for a correlational power analysis (CPA) attack, thereby increasing the threat surface for embedded devices significantly. Even for low SNR scenarios, the proposed X-DeepSCA attack achieves $\sim10\times$ lower minimum traces to disclosure (MTD) compared to a traditional CPA.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Published elsewhere. Minor revision. IEEE/ACM DAC 2019
- DOI
- 10.1145/3316781.3317934
- Keywords
- Side-channel AttacksProfiling attacksCross-device AttackDeep LearningNeural Networks.
- Contact author(s)
- das60 @ purdue edu
- History
- 2019-07-16: received
- Short URL
- https://ia.cr/2019/818
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2019/818,
author = {Debayan Das and Anupam Golder and Josef Danial and Santosh Ghosh and Arijit Raychowdhury and Shreyas Sen},
title = {X-{DeepSCA}: Cross-Device Deep Learning Side Channel Attack},
howpublished = {Cryptology {ePrint} Archive, Paper 2019/818},
year = {2019},
doi = {10.1145/3316781.3317934},
url = {https://eprint.iacr.org/2019/818}
}
