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Title: X-DeepSCA: Cross-Device Deep Learning Side Channel Attack
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 ≤ 10) 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 ~10× lower minimum traces to disclosure (MTD) compared to a traditional CPA.
Authors:
Award ID(s):
1719235
Publication Date:
NSF-PAR ID:
10123828
Journal Name:
2019 56th ACM/IEEE Design Automation Conference (DAC)
Sponsoring Org:
National Science Foundation
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