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2015 | OriginalPaper | Chapter

Robust Profiling for DPA-Style Attacks

Authors : Carolyn Whitnall, Elisabeth Oswald

Published in: Cryptographic Hardware and Embedded Systems -- CHES 2015

Publisher: Springer Berlin Heidelberg

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Abstract

Profiled side-channel attacks are understood to be powerful when applicable: in the best case when an adversary can comprehensively characterise the leakage, the resulting model leads to attacks requiring a minimal number of leakage traces for success. Such ‘complete’ leakage models are designed to capture the scale, location and shape of the profiling traces, so that any deviation between these and the attack traces potentially produces a mismatch which renders the model unfit for purpose. This severely limits the applicability of profiled attacks in practice and so poses an interesting research challenge: how can we design profiled distinguishers that can tolerate (some) differences between profiling and attack traces?

This submission is the first to tackle the problem head on: we propose distinguishers (utilising unsupervised machine learning methods, but also a ‘down-to-earth’ method combining mean traces and PCA) and evaluate their behaviour across an extensive set of distortions that we apply to representative trace data. Our results show that the profiled distinguishers are effective and robust to distortions to a surprising extent.

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Note that, in the case that the target function is injective (e.g. the AES S-box), the ‘trivial’ nominal power model which treats each intermediate value as a distinct class invariably fails to distinguish between key hypotheses in any partition-based DPA (see [20, 25]). Therefore, a meaningful non-trivial grouping is required.

The variances in Eq. (1) are squared as per [4]; this makes the univariate VR slightly different to the original definition given in [20], but (importantly) consistent with the multivariate version.

Defined as the height of the individual link minus the mean height of all links at the same hierarchical level, all divided by the standard deviation of all the heights on that level.

In particular, in the notation of Sect. 2.1, the extent to which \(\{z' | M(z') = M(z)\} \approx \{z' | L(z') = L(z)\} \forall z \in \mathcal {Z}\)—see [25].

Note that this process involves centering around the global mean, thereby avoiding the DC offset problems highlighted by [7].

All our data stems from real devices: one implementation of AES on an ARM7 processor, and one implementation of AES in dedicated hardware (an ASIC custom-built for the TAMPRES project [1, 13]) using a 32-bit architecture but with a serial S-box look-up. In order to create data sets with different characteristics we did however not change the measurement setups as this would have been a too cumbersome process. Instead we manipulated the original data sets and hence, strictly speaking, the distorted data was created by simulations.

The different sample sizes reflect the fact that we sourced independently-generated datasets for our experiments rather than relying on acquisition set-ups over which we had full control.

Identified by using the (point-wise) conditional means as optimal power models in (point-wise) correlation DPA, and selecting the one giving the strongest margin of success.

Computed as the mean of the preceding and following measurements.

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Title: Robust Profiling for DPA-Style Attacks
Authors: Carolyn Whitnall
Elisabeth Oswald
Publisher: Springer Berlin Heidelberg
Book: Cryptographic Hardware and Embedded Systems -- CHES 2015
Print ISBN: 978-3-662-48323-7

Electronic ISBN: 978-3-662-48324-4

Copyright Year: 2015
DOI: https://doi.org/10.1007/978-3-662-48324-4_1

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