From Homogeneous to Heterogeneous: Leveraging Deep Learning based Power Analysis across Devices

In this paper, we raise practical situations in profiling based power analysis when profiling and target devices are quite different at several levels. "Crossed devices" are newly termed, including homogeneous and heterogeneous devices, which have not been carefully investigated. We identify such device variations and take a further step towards leveraging the deep learning based power analysis. Traditional template attacks and straight-forward deep learning based power analysis will fail, when the gap across devices is significantly enlarged. In this paper, we propose a noval frequency and learning based power analysis machanism, which is able to explore new attacking power of deep learning and address challenges caused by device variations. For the first time, power traces collected from our own PIC devices can be utilized to successfully attack the public dataset in DPAContest v4 which is based on a totally different AVR microcontroller.