Machine Learning Assisted Side-Channel-Attack Countermeasure and Its Application on a 28-nm AES Circuit

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.