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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.
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