The investigation of neural networks performance in side-channel attacks

Scientists have devoted a lot of affords to guarantee the safety of cryptosystems by improving cryptography algorithms, while these systems can still be vulnerable to side-channel information analysis based on neural networks (NNs) and principal component analysis (PCA). PCA can be used as a preprocessing stage, while NNs can learn the signature (power consumption and electromagnetic emission) of an instruction of a cryptography algorithm, and then recognizes it later automatically. This paper investigate the performance of NNs as a powerful classifier to analysis the side-channel information. For this purpose, an experimental investigation was conducted based on the power consumption and electromagnetic emission analysis of a field-programmable gate array implementation of elliptic curve cryptography. In our experimental results, the performance of different NNs topologies are compared which provide useful information for cryptosystem designers. In addition an efficient NN topology is introduced for characterization of side-channel information.