Cryptology ePrint Archive: Report 2019/818

X-DeepSCA: Cross-Device Deep Learning Side Channel Attack

Debayan Das and Anupam Golder and Josef Danial and Santosh Ghosh and 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.

Category / Keywords: applications / Side-channel Attacks, Profiling attacks, Cross-device Attack, Deep Learning, Neural Networks.

Original Publication (with minor differences): IEEE/ACM DAC 2019
DOI:
10.1145/3316781.3317934

Date: received 14 Jul 2019

Contact author: das60 at purdue edu

Available format(s): PDF | BibTeX Citation

Version: 20190716:122329 (All versions of this report)

Short URL: ia.cr/2019/818


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