Revealing the Weakness of Addition Chain Based Masked SBox Implementations

Ming, Jingdian; Li, Huizhong; Zhou, Yongbin; Cheng, Wei; Qiao, Zehua

doi:10.46586/tches.v2021.i4.326-350

Cited by 5 publications

(12 citation statements)

References 21 publications

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“…ey assumed that the leakages in each operation (square or multiplication) are under the same noisy model, meanwhile sensitive information in these leakages follows the same bound related to a noise parameter. It can be seen as a simplified scenario, but the leakages from each operation are actually different and related to the function itself [38].…”

Section: Related Workmentioning

confidence: 99%

“…en, Ming et al [38] investigated how different operations impact the security of addition chain implementations. However, they mainly studied the resistance of addition chains against nonprofiled SCAs.…”

Section: Related Workmentioning

confidence: 99%

“…erefore, the adversary can use much fewer traces to attack the computation of certain monomials rather than S-Box outputs [38].…”

Section: Addition-chain-based Masked S-boxesmentioning

confidence: 99%

“…As shown in Table 1, all monomials can be grouped into 7 classes in the sense of output size. And monomials with the same output size can be viewed as equivalent when using MI metric [38]. Considering d � 1 in the first-order Boolean masking, the MI results of AES S-Box and monomial functions are depicted in Figure 2.…”

Section: Analysis Based On Information-eoretic Metricmentioning

confidence: 99%

“…Besides, with the increase of the output size, the difference between the MI values of different functions becomes very small. Moreover, in Ming et al's study [38], it was also pointed out that the MI metric fails to work when evaluating the CPA resistance of different monomials.…”

Section: Analysis Based On Information-eoretic Metricmentioning

confidence: 99%

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Assessment of Addition-Chain-Based Masked S-Box Using Deep-Learning-Based Side-Channel Attacks

Ming

Zhou

2022

Security and Communication Networks

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Masking schemes are considered to be effective countermeasures to protect Internet-of-Things devices from side-channel attacks. Deep-learning-based side-channel attacks (DL-SCAs) have been demonstrated to be very effective targeting on masked implementations. In this paper, we investigate the resistance of a popular computation-based masking scheme against DL-SCAs, that is, the addition-chain-based one. We find that addition chain introduces computations of intermediate monomials over F 2 n with smaller output sizes, which decreases its resistance against DL-SCAs. Specifically, we first use mutual information metric to evaluate the side-channel resistance of different monomials from an information theory point of view. Next, we further propose the Kullback–Leibler divergence ratio as an evaluation metric to analyze the impact of monomial output size on DL-SCAs. The measurement values show that the monomial with smaller output size is less-resistant against DL-SCAs. Then we conduct simulated and practical experiments respectively to verify it. In simulated experiments, we perform DL-SCAs on first-order masked implementations with different noise levels and training trace numbers. The results demonstrate that monomials with smaller output size are more vulnerable. Moreover, with the increase (resp. decrease) in noise level (resp. training trace number), the resistance difference of these monomials becomes more significant. In addition, we obtain similar results through simulated experiments on second-order masked scenario. In practical experiments based on an ARM Cortex-M4 architecture, we collect power and electromagnetic traces in consideration of low and high noise levels. The results show that the number of required traces for targeting the S-Box output is at least three times as much that for targeting the weakest monomial.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…erefore, the adversary can use much fewer traces to attack the computation of certain monomials rather than S-Box outputs [38].…”

Section: Addition-chain-based Masked S-boxesmentioning

confidence: 99%

Section: Analysis Based On Information-eoretic Metricmentioning

confidence: 99%

Section: Analysis Based On Information-eoretic Metricmentioning

confidence: 99%

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Assessment of Addition-Chain-Based Masked S-Box Using Deep-Learning-Based Side-Channel Attacks

Ming

Zhou

2022

Security and Communication Networks

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Amplitude-modulated EM side-channel attack on provably secure masked AES

Wang

2024

J Cryptogr Eng

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Recently a new type of side channels was discovered, called amplitude-modulated electromagnetic (EM) emanations from mixed-signal circuits. Unlike power analysis or near field EM analysis, attacks based on amplitude-modulated EM emanations do not require the close physical access to the victim device, which makes the attack particularly threatening. However, all existing amplitude-modulated EM attacks on AES focus on implementations of unprotected TinyAES, which is less likely to be used when the implementation is not overly resource constrained. This paper presents the first deep learning based side-channel attack on AES-128 with a Rivain–Prouff masking scheme by using amplitude-modulated EM emanations as the side channel. Rivian–Prouff masking scheme is a provably secure higher-order masking scheme for AES. To bypass the theoretical strength of the addition-chain based Boolean masked SBox, we train neural networks on trace segments corresponding to the MixColumns operation in which the data loading instructions for SBox output leak information. By comparing two different training strategies, we show that it is feasible to recover the key from an ARM Cortex-M4 CPU implementation of AES-128 with a Rivain–Prouff masking scheme by using the amplitude-modulated EM emanations leaked from the victim device, which has a Bluetooth module embedded on the board.

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