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
Creative Commons Attribution
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},
      note = {\url{https://eprint.iacr.org/2019/818}},
      url = {https://eprint.iacr.org/2019/818}
}
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.