Fault injection-based cryptanalysis is one of the most powerful practical threats to modern cryptographic primitives. Popular countermeasures to such fault-based attacks generally use some form of redundant computation to detect and react/correct the injected faults. However, such countermeasures are shown to be vulnerable to selective fault injections. In this article, we aim to develop a cryptographic primitive that is fault tolerant by its construction and does not require to compute the same value multiple times. We utilize the effectiveness of Neural Networks (NNs), which show “some degree” of robustness by functioning correctly even after the occurrence of faults in any of its parameters. We also propose a novel strategy that enhances the fault tolerance of the implementation to “high degree” (close to 100%) by incorporating selective constraints in the NN parameters during the training phase. We evaluated the performance of revised NN considering both software and FPGA implementations for standard cryptographic primitives like 8×8 AES SBox and 4×4 PRESENT SBox. The results show that the fault tolerance of such implementations can be significantly increased with the proposed methodology. Such NN-based cryptographic primitives will provide inherent resistance against fault injections without requiring any redundancy countermeasures.

中文翻译：

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没有显式冗余检查的基于神经网络的固有容错硬件密码原语

基于故障注入的密码分析是现代密码原语最强大的实际威胁之一。针对这种基于故障的攻击的流行对策通常使用某种形式的冗余计算来检测和反应/纠正注入的故障。然而，这种对策被证明容易受到选择性故障注入的影响。在本文中，我们的目标是开发一种加密原语通过其构造容错并且不需要多次计算相同的值。我们利用神经网络 (NNs) 的有效性，即使在其任何参数出现故障之后，它也能通过正确运行来显示“一定程度”的鲁棒性。我们还提出了一种新颖的策略，通过在训练阶段在 NN 参数中加入选择性约束，将实现的容错性提高到“高度”（接近 100%）。我们评估了修订后的 NN 的性能，同时考虑了标准密码原语（如 8×8 AES SBox 和 4×4 PRESENT SBox）的软件和 FPGA 实现。结果表明，使用所提出的方法可以显着提高此类实现的容错性。