Abstract
Template attacks remain a most powerful side-channel technique 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 obtained during profiling are used to infer some secret value (e.g. cryptographic 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 template 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 unknown 8-bit value below 1.5 bits even when using different devices.
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Notes
- 1.
Throughout this paper \({\mathbf {x}}'\) is the transpose of \(\mathbf {x}\).
- 2.
The SNR signal-strength estimate generally provided similar results (omitted here).
- 3.
In our setting we cannot use the non-profiled stochastic method (termed on-the-fly attacks by Renauld et al. [12]) either, because our attacker only has data dependent on the target secret value.
- 4.
- 5.
We used the median value of \(\bar{\mathbf {x}}_{}^{\text {r}(\mathrm {profile})}\) as the offset, since it provides a very good approximation with our data. However, when using a higher clock frequency, the median can become very noisy, so we might have to find more robust methods.
- 6.
We have chosen \(c_k\) uniformly from the interval \([-u,u]\), where \(u\) is the absolute average offset between the overall mean vectors shown in Fig. 3.
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Acknowledgement
Omar Choudary is a recipient of the Google Europe Fellowship in Mobile Security, and this research is supported in part by this Google Fellowship. The opinions expressed in this paper do not represent the views of Google unless otherwise explicitly stated.
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Choudary, O., Kuhn, M.G. (2014). Template Attacks on Different Devices. In: Prouff, E. (eds) Constructive Side-Channel Analysis and Secure Design. COSADE 2014. Lecture Notes in Computer Science(), vol 8622. Springer, Cham. https://doi.org/10.1007/978-3-319-10175-0_13
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