Deep Learning-Based Differential Distinguisher for Lightweight Cipher GIFT-COFB

Rajan, Reshma; Roy, Rupam Kumar; Sen, Diptakshi; Mishra, G. C.

doi:10.1007/978-981-16-9650-3_31

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Side channel Elephant KRA using CPA -recovers the key for the 160-bit variant in about a minute using 35 power traces [55] GIFT-COFB KRA with DCSCA -TC: 2 18.39 and MC: 2 25.39 , the TC is 2 13.39 if 32 encryptions per session are assumed Others GIFT-COFB KRA -TC:2 4 for 2-round GIFT-COFB [56]; FA -success probability qd/2 n/2 , given qd forgery attempts, also shows FA possible with 2 n/2 attempts using a single known-plaintext encryption query [57]; DA -2 and 6 rounds of GIFT-COFB [58]; updated bound for privacy [59] PHOTON-Beetle KRA, QC: 2 22.8 [60]; related key FA with an updated security bound of the authentication [59] Romulus MA on Romulus-M [61] SPARKLE MITM based DA -Sparkle-256/384/512 with 4/4/5 steps, respectively, with practical TCs and MCs [62] TinyJambu DA [63] -TC/DC: 2 23 for 544-round in secret-key settings, TC/DC: 2 16 /2 23 for full-round 128/192-bit versions in known-key settings, TC/DC: 2 23 for 1152-round 256-bit version in known-key settings Xoodyak DA-12-round Xoodoo (permutation of Xoodyak) with TC:2 33 [64]…”

Section: Summary Of Existing Cryptanalyses Of Nist Lwc Finalist Algor...mentioning

confidence: 99%

A Review of the NIST Lightweight Cryptography Finalists and Their Fault Analyses

2022

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The security of resource-constrained devices is critical in the IoT field, given that everything is interconnected. Therefore, the National Institute of Standards and Technology (NIST) initialized the lightweight cryptography (LWC) project to standardize the lightweight cryptography algorithms for resource-constrained devices. After two rounds, the NIST announced the finalists in 2021. The finalist algorithms are Ascon, Elephant, GIFT-COFB, Grain-128AEAD, ISAP, PHOTON-Beetle, Romulus, SPARKLE, TinyJambu, and Xoodyak. The final round of the competition is still in progress, and the NIST will select the winner based on their and third-party evaluations. In this paper, we review the 10 finalists mentioned above, discuss their constructions, and classify them according to the underlying primitives. In particular, we analyze these ciphers from different perspectives, such as cipher specifications and structures, design primitives, security parameters, advantages and disadvantages, and existing cryptanalyses. We also review existing analyses of these finalists with a specific focus on the review of fault attacks. We hope the study compiled in this paper will benefit the cryptographic community by providing an easy-to-grasp overview of the NIST LWC finalists.

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Section: Summary Of Existing Cryptanalyses Of Nist Lwc Finalist Algor...mentioning

confidence: 99%

A Review of the NIST Lightweight Cryptography Finalists and Their Fault Analyses

2022

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“…Recently, with the development of deep learning [2][3][4][5][6][7][8], various studies on deeplearning-based distinguishers [9,10] have been presented [11][12][13][14][15][16][17][18][19][20][21]. Deep learning is wellsuited for probabilistically distinguishing data that satisfy differential characteristics, as it has the capability to make probabilistic predictions about data.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Neural Distinguisher for Format-Preserving Encryption Schemes FF1 and FF3

Kim,

Jang

et al. 2024

Electronics

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Distinguishing data that satisfy the differential characteristic from random data is called a distinguisher attack. At CRYPTO’19, Gohr presented the first deep-learning-based distinguisher for round-reduced SPECK. Building upon Gohr’s work, various works have been conducted. Among many other works, we propose the first neural distinguisher using single and multiple differences for format-preserving encryption (FPE) schemes FF1 and FF3. We harnessed the differential characteristics used in FF1 and FF3 classical distinguishers. They used SKINNY as the inner encryption algorithm for FF3. On the other hand, we employ the standard FF1 and FF3 implementations with AES encryption (which may be more robust). This work utilizes the differentials employed in FF1 and FF3 classical distinguishers. In short, when using a single 0x0F (resp. 0x08) differential, we achieve the highest accuracy of 0.85 (resp. 0.98) for FF1 (resp. FF3) in the 10-round (resp. 8-round) number domain. In the lowercase domain, due to an increased number of plaintext and ciphertext combinations, we can distinguish with the highest accuracy of 0.52 (resp. 0.55) for FF1 (resp. FF3) in a maximum of 2 rounds. Furthermore, we present an advanced neural distinguisher designed with multiple differentials for FF1 and FF3. With this sophisticated model, we still demonstrate valid accuracy in guessing the input difference used for encryption.

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“…That is, since a differential characteristic exists with a certain probability and can be predicted probabilistically, deep learning is suitable as a solution for this. Since Gohr’s work [ 4 ], numerous studies on deep-learning-based distinguishers have been conducted [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. In addition, as quantum computers have been developed in recent years, a quantum neural network utilizing a quantum computer is attracting attention.…”

Section: Introductionmentioning

confidence: 99%

Quantum Neural Network Based Distinguisher on SPECK-32/64

Kim

Jang

Lim

et al. 2023

Sensors

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As IoT technology develops, many sensor devices are being used in our life. To protect such sensor data, lightweight block cipher techniques such as SPECK-32 are applied. However, attack techniques for these lightweight ciphers are also being studied. Block ciphers have differential characteristics, which are probabilistically predictable, so deep learning has been utilized to solve this problem. Since Gohr’s work at Crypto2019, many studies on deep-learning-based distinguishers have been conducted. Currently, as quantum computers are developed, quantum neural network technology is developing. Quantum neural networks can also learn and make predictions on data, just like classical neural networks. However, current quantum computers are constrained by many factors (e.g., the scale and execution time of available quantum computers), making it difficult for quantum neural networks to outperform classical neural networks. Quantum computers have higher performance and computational speed than classical computers, but this cannot be achieved in the current quantum computing environment. Nevertheless, it is very important to find areas where quantum neural networks work for technology development in the future. In this paper, we propose the first quantum neural network based distinguisher for the block cipher SPECK-32 in an NISQ. Our quantum neural distinguisher successfully operated for up to 5 rounds even under constrained conditions. As a result of our experiment, the classical neural distinguisher achieved an accuracy of 0.93, but our quantum neural distinguisher achieved an accuracy of 0.53 due to limitations in data, time, and parameters. Due to the constrained environment, it cannot exceed the performance of classical neural networks, but it can operate as a distinguisher because it has obtained an accuracy of 0.51 or higher. In addition, we performed an in-depth analysis of the quantum neural network’s various factors that affect the performance of the quantum neural distinguisher. As a result, it was confirmed that the embedding method, the number of the qubit, and quantum layers, etc., have an effect. It turns out that if a high-capacity network is needed, we have to properly tune properly to take into account the connectivity and complexity of the circuit, not just by adding quantum resources. In the future, if more quantum resources, data, and time become available, it is expected that an approach to achieve better performance can be designed by considering the various factors presented in this paper.

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Deep Learning-Based Differential Distinguisher for Lightweight Cipher GIFT-COFB

Cited by 4 publications

References 5 publications

A Review of the NIST Lightweight Cryptography Finalists and Their Fault Analyses

A Review of the NIST Lightweight Cryptography Finalists and Their Fault Analyses

Deep-Learning-Based Neural Distinguisher for Format-Preserving Encryption Schemes FF1 and FF3

Quantum Neural Network Based Distinguisher on SPECK-32/64

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