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null (Ed.)Applications such as secure authentication, remote health monitoring require secure, low power communication between devices around the body. Radio wave communication protocols, such as Bluetooth, suffer from the problem of signal leakage and high power requirement. Electro Quasistatic Human Body Communication (EQS-UBC) is the ideal alternative as it confines the signal within the body and also operates at order of magnitude lower power. In this paper, we design a secure HBC SoC node, which uses EQS-UBC for physical security and an AES-256 core for mathematical security. The SoC consumes 415nW power with an active power of 108nW for a data rate of 1kbps, sufficient for authentication and remote monitoring applications. This translates to 100x improvement in power consumption compared to state-of-the-art HBC implementations while providing physical security for the first time.more » « less
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null (Ed.)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.more » « less
