Template Attacks on Different Devices

Abstract. Template attacks remain a most powerful side-channel tech-nique to eavesdrop on tamper-resistant hardware. They use a profiling step to compute the parameters of a multivariate normal distribution from a training device and an attack step in which the parameters ob-tained during profiling are used to infer some secret value (e.g. crypto-graphic key) on a target device. Evaluations using the same device for both profiling and attack can miss practical problems that appear when using different devices. Recent studies showed that variability caused by the use of either different devices or different acquisition campaigns on the same device can have a strong impact on the performance of tem-plate attacks. In this paper, we explore further the effects that lead to this decrease of performance, using four different Atmel XMEGA 256 A3U 8-bit devices. We show that a main difference between devices is a DC offset and we show that this appears even if we use the same device in different acquisition campaigns. We then explore several variants of the template attack to compensate for these differences. Our results show that a careful choice of compression method and parameters is the key to improving the performance of these attacks across different devices. In particular we show how to maximise the performance of template attacks when using Fisher’s Linear Discriminant Analysis or Principal Component Analysis. Overall, we can reduce the entropy of an unknow