Quadruplet network based template attack

Abstract The raw traces collected in side channel attacks are composed of high-dimensional signals with severe noise, which significantly increases the computational complexity of an attack, reduces the performance and the accuracy of an attack, and may even make an attack infeasible. In order to efficiently extract the critical and low-dimensional features from traces to optimize the accuracy of an attack, quadruplet network based template attack is proposed. Firstly, a quadruple sample selection strategy is designed to screen out quadruples that meet the requirements, and a quadruple loss function is designed to measure the similarity between trace sample pairs. Secondly, a quadruplet network model based on densely connected convolutional networks is constructed and trained to obtain an optimal quadruplet network model. Further, the critical and low-dimensional embedded features are extracted by the optimal quadruplet network model. Finally, efficient templates are constructed based on the embedded features to perform template attack. The test results show that the proposed method can efficiently extract the key embedded features from traces to construct efficient templates and optimize the efficiency of template attack. Compared with its counterparts, this technique can optimize the efficiency of template attack on different datasets.