A Machine Learning Approach Against a Masked AES

Lerman, Liran; Medeiros, Stephane Fernandes; Bontempi, Gianluca; Markowitch, Olivier

doi:10.1007/978-3-319-08302-5_5

Cited by 30 publications

(17 citation statements)

References 19 publications

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“…Lerman et al [47] presented a machine learning attack against a masking countermeasure, using the dataset of the DPA Contest v4. The method of power analysis based on a multi-layer perceptron was first presented in [48].…”

Section: Related Workmentioning

confidence: 99%

“…ML approaches have not been compared yet. The work [47] can be mentioned as an exception, where SVM and RF are compared with the TA and the SA. In this article, we try to make an extensive comparison of machine learning algorithms in PA. We focus only on the usage of the individual ML algorithms in profiling attacks where ML techniques are used for a model creation of the target device.…”

Section: Contributionmentioning

confidence: 99%

“…The third dataset (DS3) was created by Liran Lerman during preparation of the attack in DPA Contest v4 [47]. This DS is focused on the mask classification and we used first 1 000 traces of 1 500 available.…”

Section: Settings Of Experimentsmentioning

confidence: 99%

“…Again, the label value corresponds with the offset value 0 to 15 (sixteen possible variants). We refer the work [47] for more information about the original dataset. An example of power traces selected is depicted in Fig.…”

Section: Settings Of Experimentsmentioning

confidence: 99%

See 3 more Smart Citations

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Malina¹,

Zeman²,

Martinasek³

et al. 2016

RADIOENGINEERING

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show abstract

Section: Related Workmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Settings Of Experimentsmentioning

confidence: 99%

Section: Settings Of Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Malina¹,

Zeman²,

Martinasek³

et al. 2016

RADIOENGINEERING

View full text Add to dashboard Cite

show abstract

“…More precisely, the authors assumed that the side channel leakage information could split interesting points into a strict order. Lerman et al [27] presented the attack based on machine learning algorithm against the masked AES implementation. The results declared that SVM required 26 power traces to recover the key and had smaller computational complexity than TA.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Support Vector Machine Algorithm in Power Analysis Attacks

Hou¹,

Zhou²,

Liu³

et al. 2017

RADIOENGINEERING

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Crucial pitfall of DPA Contest V4.2 implementation

Martinásek

Iglesias

Malina

et al. 2016

Security Comm Networks

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Differential power analysis (DPA) is a powerful side‐channel key recovery attack that efficiently breaks cryptographic algorithm implementations. In order to prevent these types of attacks, hardware designers and software programmers make use of masking and hiding techniques. DPA contest is an international framework that allows researchers to compare their power analysis attacks under the same conditions. The latest version of DPA contest, denoted as V4.2, provides an improved implementation of the rotating S‐box masking scheme where low‐entropy boolean masking is combined with the shuffling technique to protect Advanced Encryption Standard implementation on a smart card. The improvements were designed based on the awareness of implementation lacks analyzed from attacks carried out during the previous DPA contest V4. Therefore, this new approach is devised to resist most of the proposed attacks to the original rotating S‐box masking implementation. In this paper, we investigate the security of this new implementation in practice. Our analysis, focused on exploiting the first‐order leakage, discovered important lacks. The main vulnerability observed is that an adversary can mount a standard DPA attack aimed at the S‐box output in order to recover the whole secret key even when a shuffling technique is used. We tested this observation on a public dataset and implemented a successful attack that revealed the secret key using only 35 power traces. Copyright © 2017 John Wiley & Sons, Ltd.

show abstract

A Machine Learning Approach Against a Masked AES

Cited by 30 publications

References 19 publications

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Wavelet Support Vector Machine Algorithm in Power Analysis Attacks

Crucial pitfall of DPA Contest V4.2 implementation

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