Improving CEMA using Correlation Optimization

Robyns, Pieter; Quax, Peter; Lamotte, Wim

doi:10.46586/tches.v2019.i1.1-24

Cited by 18 publications

(12 citation statements)

References 19 publications

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“…We perform our DL-based evaluation based on the Correlation Optimization (CO) scheme introduced by Robyns et al [RQL18] at CHES 2019. It is shortly introduced in the following before we present two extensions to the original CO scheme.…”

Section: Methodsmentioning

confidence: 99%

“…Next, we present an attack procedure that allows to extract the 256-bit key involving the first five AES rounds. Finally, we demonstrate the application of our attack procedure using an enhanced version of the DL-based correlation optimization scheme introduced at CHES 2019 [RQL18]. Although we are not able to recover all bytes of the AES-256 key in our experiments, we can provide concrete numbers about the remaining attack complexity.…”

Section: Contributionmentioning

confidence: 98%

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Side-Channel Analysis of the Xilinx Zynq UltraScale+ Encryption Engine

Hettwer

Leger

Fennes

et al. 2020

TCHES

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The Xilinx Zynq UltraScale+ (ZU+) is a powerful and flexible System-on- Chip (SoC) computing platform for next generation applications such as autonomous driving or industrial Internet-of-Things (IoT) based on 16 nm production technology. The devices are equipped with a secure boot mechanism in order to provide confidentiality, integrity, and authenticity of the configuration files that are loaded during power-up. This includes a dedicated encryption engine which features a protocol-based countermeasure against passive Side-Channel Attacks (SCAs) called key rolling. The mechanism ensures that the same key is used only for a certain number of data blocks that has to be defined by the user. However, a suitable choice for the key rolling parameter depends on the power leakage behavior of the chip and is not published by the manufacturer. To close this gap, this paper presents the first publicly known side-channel analysis of the ZU+ encryption unit. We conduct a black-box reverse engineering of the internal hardware architecture of the encryption engine using Electromagnetic (EM) measurements from a decoupling capacitor of the power supply. Then, we illustrate a sophisticated methodology that involves the first five rounds of an AES encryption to attack the 256-bit secret key. We apply the elaborated attack strategy using several new Deep Learning (DL)-based evaluation methods for cryptographic implementations. Even though we are unable to recover all bytes of the secret key, the experimental results still allow us to provide concrete recommendations for the key rolling parameter under realistic conditions. This eventually helps to configure the secure boot mechanism of the ZU+ and similar devices appropriately.

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Section: Methodsmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 98%

Side-Channel Analysis of the Xilinx Zynq UltraScale+ Encryption Engine

Hettwer

Leger

Fennes

et al. 2020

TCHES

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“…We propose an architecture that encodes measurements using shared layers to reduce the dimensions of the measurements before transferring them to each base architecture. A similar approach to this concept, which encodes side-channel measurements using deep learning was proposed by Robyns [25]. Also, in [26], they also separated a neural network for Points of Interest (PoI) detection and plaintext feature embedding.…”

Section: Improved Differential Deep Learning Analysis With Shared Layersmentioning

confidence: 99%

Optimizing Implementations of Non-Profiled Deep Learning-Based Side-Channel Attacks

2022

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The differential deep learning analysis proposed by Timon is the first non-profiled sidechannel attack technique that uses deep learning. The technique recovers the secret key using the phenomenon of deep learning metrics. However, the proposed technique made it difficult to observe the results from the intermediate process, while the neural network had to be retrained repeatedly, as the cost of learning increased with key size. In this paper, we propose three methods to solve the aforementioned problems and any challenges resulting from solving these problems. First, we propose a modified algorithm that allows the monitoring of the metrics in the intermediate process. Second, we propose a parallel neural network architecture and algorithm that works by training a single network, with no need to re-train the same model repeatedly. Attacks were performed faster with the proposed algorithm than with the previous algorithm. Finally, we propose a novel architecture that uses shared layers to solve memory problems in parallel architecture and also helps achieve better performance. We validated the performance of our methods by presenting the results of non-profiled attacks on the benchmark database ASCAD and for a custom dataset on power consumption collected from ChipWhisperer-Lite. On the ASCAD database, our shared layers method was up to 134 times more efficient than the previous method.INDEX TERMS Side-channel attacks, deep learning, non-profiled attack, differential deep learning analysis, efficient implementations

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“…Since Kocher proposed the timing analysis method [ 1 ] in 1996, side-channel analysis, a unique cryptanalysis method distinct from classical cryptanalysis, has become a research hotspot in the field of cryptography, after more than 20 years of development with its powerful analysis ability and wide application range. General classes of side-channel analysis include timing analysis, power analysis [ 2 , 3 , 4 , 5 , 6 , 7 ], template analysis [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ], electromagnetic analysis [ 14 , 15 , 16 ], collision attack [ 17 , 18 ], fault analysis [ 19 , 20 , 21 ], and artificial intelligence side-channel analysis [ 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Second-Order Side-Channel Analysis Based on Orthogonal Transform Nonlinear Regression

Jiang

Ding

2023

Entropy

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In recent years, side-channel analysis technology has been one of the greatest threats to information security. SCA decrypts the key information in the encryption device by establishing an appropriate leakage model. As one of many leakage models, the XOR operation leakage proposed by linear regression has typical representative significance in side-channel analysis. However, linear regression may have the problem of irreversibility of a singular matrix in the modeling stage of template analysis and the problem of poor data fit in the template analysis after the cryptographic algorithm is masked. Therefore, this paper proposes a second-order template analysis method based on orthogonal transformation nonlinear regression. The irreversibility of a singular matrix and the inaccuracy of the model are solved by orthogonal transformation and adding a negative direction to the calculation of the regression coefficient matrix. In order to verify the data fitting effect of the constructed template, a comparative experiment of template analysis based on regression, Gaussian, and clustering was carried out on SAKURA-G. The experimental results show that the second-order template analysis based on orthogonal transformation nonlinear regression can complete key recovery without sacrificing the performance of regression estimation. Under the condition of high noise and high order template analysis, the established template has good universality.

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Improving CEMA using Correlation Optimization

Cited by 18 publications

References 19 publications

Side-Channel Analysis of the Xilinx Zynq UltraScale+ Encryption Engine

Side-Channel Analysis of the Xilinx Zynq UltraScale+ Encryption Engine

Optimizing Implementations of Non-Profiled Deep Learning-Based Side-Channel Attacks

Second-Order Side-Channel Analysis Based on Orthogonal Transform Nonlinear Regression

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