LDA-Based Clustering as a Side-Channel Distinguisher

Mahmudlu, Rauf; Banciu, Valentina; Batina, Lejla; Buhan, Ileana

doi:10.1007/978-3-319-62024-4_5

Cited by 4 publications

(8 citation statements)

References 17 publications

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“…On the other hand, it has also recently been proposed for use directly as a DPA distinguisher [12]. To this end it operates as follows: sort the total power consumption…”

Section: Kernel Discriminant Analysismentioning

confidence: 99%

“…Meanwhile, Mahmudlu et al [12] have shown that the largest eigenvalue, which measures the (optimised) between-to within-scatter matrix ratio under a particular key guess, is typically higher for a correct guess (which produces a meaningful labelling on the traces) than an incorrect one (which produces a random labelling), thereby functioning as an effective distinguishing score.…”

Section: Theoretical Rationalementioning

confidence: 99%

See 1 more Smart Citation

A Novel Use of Kernel Discriminant Analysis as a Higher-Order Side-Channel Distinguisher

Zhou

Whitnall

Oswald

et al. 2018

Smart Card Research and Advanced Applications

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Abstract. Distinguishers play an important role in Side Channel Analysis (SCA), where real world leakage information is compared against hypothetical predictions in order to guess at the underlying secret key. However, the direct relationship between leakages and predictions can be disrupted by the mathematical combining of d random values with each sensitive intermediate value of the cryptographic algorithm (a so-called "d-th order masking scheme"). In the case of software implementations, as long as the masking has been correctly applied, the guessable intermediates will be independent of any one point in the trace, or indeed of any tuple of fewer than d + 1 points. However, certain d + 1-tuples of time points may jointly depend on the guessable intermediates. A typical approach to exploiting this data dependency is to pre-process the trace -computing carefully chosen univariate functions of all possible d + 1-tuples -before applying the usual univariate distinguishers. This has a computational complexity which is exponential in the order d of the masking scheme. In this paper, we propose a new distinguisher based on Kernel Discriminant Analysis (KDA) which directly exploits properties of the mask implementation without the need to exhaustively pre-process the traces, thereby distinguishing the correct key with lower complexity. Experimental results for 2nd and 3rd order attacks (i.e. against 1st and 2nd order masking) verify that the KDA is an effective distinguisher in protected settings.

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“…On the other hand, it has also recently been proposed for use directly as a DPA distinguisher [12]. To this end it operates as follows: sort the total power consumption…”

Section: Kernel Discriminant Analysismentioning

confidence: 99%

Section: Theoretical Rationalementioning

confidence: 99%

A Novel Use of Kernel Discriminant Analysis as a Higher-Order Side-Channel Distinguisher

Zhou

Whitnall

Oswald

et al. 2018

Smart Card Research and Advanced Applications

View full text Add to dashboard Cite

show abstract

“…, ω T . The use of LDA as a DPA distinguisher is proposed by Mahmudlu et al in [8]. Similar in procedure to FPCA, LDA-based DPA operates as follows: arrange the power consumption traces into clusters according to the key hypothesis and the power model; perform LDA on the labeled clusters; extract the first (largest) generalized eigenvalue as the distinguisher score for the key hypothesis.…”

Section: Cluster-based Distinguishersmentioning

confidence: 99%

“…Since LDA has been promoted as especially useful in scenarios exhibiting high levels of noise [8], we now explore the performance of all four distinguishers as noise increases. To do this, we simulate traces by adding Gaussian noise of increasing magnitude to the Hamming weight of intermediate value.…”

Section: Influence Of Noisementioning

confidence: 99%

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Categorising and Comparing Cluster-Based DPA Distinguishers

Zhou

Whitnall

Oswald

et al. 2017

Selected Areas in Cryptography – SAC 2017

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Abstract. Side-channel distinguishers play an important role in differential power analysis, where real world leakage information is compared against hypothetical predictions in order to guess at the underlying secret key. A class of distinguishers which can be described as 'cluster-based' have the advantage that they are able to exploit multi-dimensional leakage samples in scenarios where only loose, 'semi-profiled' approximations of the true leakage forms are available. This is by contrast with univariate distinguishers exploiting only single points (e.g. correlation), and Template Attacks requiring concise fitted models which can be overly sensitive to mismatch between the profiling and attack acquisitions. This paper collects together-to our knowledge, for the first time-the various different proposals for cluster-based DPA (concretely, Differential Cluster Analysis, First Principal Components Analysis, and Linear Discriminant Analysis), and shows how they fit within the robust 'semi-profiling' attack procedure proposed by Whitnall et al. at CHES 2015. We provide discussion of the theoretical similarities and differences of the separately proposed distinguishers as well as an empirical comparison of their performance in a range of (real and simulated) leakage scenarios and with varying parameters. Our findings have application for practitioners constrained to rely on 'semi-profiled' models who wish to make informed choices about the best known procedures to exploit such information.

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A Hybrid Approach to Formal Verification of Higher-Order Masked Arithmetic Programs

Gao

Xie

Song

et al. 2021

ACM Trans. Softw. Eng. Methodol.

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Side-channel attacks, which are capable of breaking secrecy via side-channel information, pose a growing threat to the implementation of cryptographic algorithms. Masking is an effective countermeasure against side-channel attacks by removing the statistical dependence between secrecy and power consumption via randomization. However, designing efficient and effective masked implementations turns out to be an error-prone task. Current techniques for verifying whether masked programs are secure are limited in their applicability and accuracy, especially when they are applied. To bridge this gap, in this article, we first propose a sound type system, equipped with an efficient type inference algorithm, for verifying masked arithmetic programs against higher-order attacks. We then give novel model-counting-based and pattern-matching-based methods that are able to precisely determine whether the potential leaky observable sets detected by the type system are genuine or simply spurious. We evaluate our approach on various implementations of arithmetic cryptographic programs. The experiments confirm that our approach outperforms the state-of-the-art baselines in terms of applicability, accuracy, and efficiency.

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LDA-Based Clustering as a Side-Channel Distinguisher

Cited by 4 publications

References 17 publications

A Novel Use of Kernel Discriminant Analysis as a Higher-Order Side-Channel Distinguisher

A Novel Use of Kernel Discriminant Analysis as a Higher-Order Side-Channel Distinguisher

Categorising and Comparing Cluster-Based DPA Distinguishers

A Hybrid Approach to Formal Verification of Higher-Order Masked Arithmetic Programs

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