Efficient Selection of Time Samples for Higher-Order DPA with Projection Pursuits

Durvaux, François; Standaert, François‐Xavier; Veyrat-Charvillon, Nicolas; Mairy, Jean-Baptiste; Deville, Yves

doi:10.1007/978-3-319-21476-4_3

Cited by 20 publications

(7 citation statements)

References 28 publications

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“…The communication with the Red Pitaya (for configuration and data recovery) is done thanks to the SCPI interface. 10 The Red Pitaya allows acquiring traces at a rate slightly higher than 1 trace/s. For the following, two sets of measurements can be distinguished.…”

Section: Measuring Power Leakages With Em Radiationsmentioning

confidence: 99%

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SCA-Pitaya

Durvaux

Durvaux²

2020

Digital Threats

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Athough side-channel attacks appeared more than two decades ago, they remain very little discussed by security professionals outside the academia or very specific sectors (e.g., smartcard industry, governments). However, with the increasing generalisation of Internet of Things systems, they are a threat that can no longer be ignored by the operational world. This work aims to demonstrate that side-channel attacks can be practically achieved by an attacker, with reasonable means, effort, knowledge, and time. For this purpose, the contribution of this work is twofold. First, it is shown how a side-channel attack setup exploiting power leakages through electromagnetic radiations, and making use of general-purpose and affordable equipment, can be built. The acquisition of attack power traces is made thanks to a Red Pitaya STEMlab platform coupled with a home-built radio front-end. Second, it is shown how an attack can be conducted against targets that are representative of Internet of Things devices: 8-bit and 32-bit Arduino boards. CCS Concepts: • Security and privacy → Embedded systems security; Side-channel analysis and countermeasures;

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Section: Measuring Power Leakages With Em Radiationsmentioning

confidence: 99%

“…Projection pursuit. As a second pre-processing method, this work implements a linear projection of the useful time samples using a projection pursuit approach [10]. It is based on the observation that target bytes do not leak at a single time location, as further illustrated later in Figure 11.…”

Section: Pre-processing Of the Tracesmentioning

confidence: 99%

SCA-Pitaya

Durvaux

Durvaux²

2020

Digital Threats

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“…To conclude this paper, we therefore list its applications that confirm its relevance. First from a methodological point of view, MC-DPA has been a building block to improve recent works on leakage detection/assessment [10,9,36] and for leakage certification [8] (to appear at CHES 2016). It is also a useful tool to discuss independence issues in masking proofs [6].…”

Section: Follow Up Workmentioning

confidence: 99%

Moments-Correlating DPA

Moradi

Standaert

2016

Proceedings of the 2016 ACM Workshop on Theory of Implementation Security

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We generalize correlation-enhanced power analysis collision attacks into moments-correlating DPA. The resulting distinguisher is applicable to the profiled and non-profiled (collision) settings and is able to exploit information lying in any statistical moment. It also benefits from a simple ruleof-thumb to estimate its data complexity. Experimental results show that such a tool allows answering with confidence to some important questions regarding the design of sidechannel countermeasures (e.g. what is the most informative statistical moment in the leakages of a threshold implementation). We further argue that moments-correlating DPA is a natural candidate for leakage detection tests, enjoying the simplicity of correlation power analysis and advanced features for the evaluation of higher-order attacks with an easy-to-compute confidence level.

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“…Dimensionality reductions such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), introduced to side-channel attacks in [1,40], and recently revisited in [2,7,10,11], project samples into subspaces that optimize the side-channel signal and Signalto-Noise Ratio (SNR), respectively. Other "compressive" linear transforms (using other optimization criteria) include [15,30]. Eventually, filtering typically aims at selecting the frequency band in which side-channel attacks perform best.…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation from Single Measurements Using Singular Spectrum Analysis

Pozo

Standaert

2015

Lecture Notes in Computer Science

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Singular Spectrum Analysis (SSA) is a powerful data decomposition/recompose technique that can be used to reduce the noise in time series. Compared to existing solutions aiming at similar purposes, such as frequency-based filtering, it benefits from easier-to-exploit intuitions, applicability in contexts where low sampling rates make standard frequency analyses challenging, and the (theoretical) possibility to separate a signal source from a noisy source even if both run at the same frequency. In this paper, we first describe how to apply SSA in the context of side-channel analysis, and then validate its interest in three different scenarios. Namely, we consider unprotected software, masked software, and unprotected hardware block cipher implementations. Our experiments confirm significant noise reductions in all three cases, leading to success rates improved accordingly. They also put forward the stronger impact of SSA in more challenging scenarios, e.g. masked implementations (because the impact of noise increases exponentially with the number of shares in this case), or noisy hardware implementations (because of the established connection between the amount of noise and the attacks' success rate in this case). Since noise is a fundamental ingredient for most countermeasures against side-channel attacks, we conclude SSA can be an important element in the toolbox of evaluation laboratories, in order to efficiently preprocess their measurements in a black box manner.

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Efficient Selection of Time Samples for Higher-Order DPA with Projection Pursuits

Cited by 20 publications

References 28 publications

SCA-Pitaya

SCA-Pitaya

Moments-Correlating DPA

Blind Source Separation from Single Measurements Using Singular Spectrum Analysis

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