Hardware countermeasure of side channel attack (SCA) becomes necessary to protect crypto circuits. Many countermeasures endured large area and power consumption. We propose a SCA-resistant methodology based on machine learning, which compensates the Hamming distance (HD) probability of the intermediate data directly. By making the HD probabilities unable to be distinguished from correct and incorrect sub-keys, it provides resistance to SCA. Optimum HD redistribution is obtained by a machine learning algorithm and then sent to the compensation circuit. Applied in an Advanced Encryption Standard (AES)-128 circuit, the whole compensated circuit is implemented on a 28-nm CMOS process. The experimental results show that it resists correlation-based SCA with 1.5 million traces, corresponding to 446 $\times $ improvements of measures to disclosure compared with a nonprotected AES circuit. In addition, it has no impact on the frequency and throughput rate, and its power overhead of 38% and area overhead of 36% are relatively low, making it suitable for resource-constrained encryption circuits.

中文翻译：

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机器学习辅助侧信道攻击对策及其在 28-nm AES 电路上的应用

侧信道攻击 (SCA) 的硬件对策成为保护加密电路所必需的。许多对策忍受了大面积和功耗。我们提出了一种基于机器学习的抗 SCA 方法，它直接补偿了中间数据的汉明距离 (HD) 概率。通过使 HD 概率无法与正确和不正确的子密钥区分开来，它提供了对 SCA 的抵抗力。通过机器学习算法获得最佳高清重新分配，然后发送到补偿电路。应用于高级加密标准 (AES)-128 电路，整个补偿电路是在 28-nm CMOS 工艺上实现的。实验结果表明，它可以抵抗 150 万条迹线的基于相关性的 SCA，对应于与未受保护的 AES 电路相比，披露措施的 446 $\times $ 改进。此外，它对频率和吞吐率没有影响，38%的功率开销和36%的面积开销都比较低，适用于资源受限的加密电路。