Tandem Deep Learning Side-Channel Attack Against FPGA Implementation of AES
Huanyu Wang, Elena Dubrova · 2020
The majority of recently demonstrated deep-learning side-channel attacks use a single neural network classifier to recover the key. The potential benefits of combining multiple classifiers with ensemble learning method have not been fully explored in the side-channel attack's context. In this paper, we show that, by combining several CNN classifiers which use different attack points, it is possible to considerably reduce (more than 40% on average) the number of traces required to recover the key from an FPGA implementation of AES by power analysis. We also show that not all combinations of classifiers improve the attack efficiency.