• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.5
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• pp.85-96
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• 2001

This paper describes saturation attacks on reduced-round versions of SKIPJACK. To begin with, we will show how to construct a 16-round distinguisher which distinguishes 16 rounds of SKIPJACK from a random permutation. The distinguisher is used to attack on 18(5~22) and 23(5~27) rounds of SKIPJACK. We can also construct a 20-around distinguisher based on the 16-round distinguisher. This distinguisher is used to attack on 22(1~22) and 27(1~27) rounds of SKIPJACK. The 80-bit user key of 27 rounds of SKIPJACK can be recovered with $2^{50}$ chosen plaintexts and 3\cdot 2^{75}$ encryption times.

• Journal of Platform Technology
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• v.11 no.1
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• pp.72-84
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• 2023

With the rise of the Internet of Things, the security of such lightweight computing environments has become a hot topic. Lightweight block ciphers that can provide efficient performance and security by having a relatively simpler structure and smaller key and block sizes are drawing attention. Due to these characteristics, they can become a target for new attack techniques. One of the new cryptanalytic attacks that have been attracting interest is Neural cryptanalysis, which is a cryptanalytic technique based on neural networks. It showed interesting results with better results than the conventional cryptanalysis method without a great amount of time and cryptographic knowledge. The first work that showed good results was carried out by Aron Gohr in CRYPTO'19, the attack was conducted on the lightweight block cipher SPECK-/32/64 and showed better results than conventional differential cryptanalysis. In this paper, we first apply the Differential Neural Distinguisher proposed by Aron Gohr to the block ciphers HIGHT and GOST to test the applicability of the attack to ciphers with different structures. The performance of the Differential Neural Distinguisher is then analyzed by replacing the neural network attack model with five different models (Multi-Layer Perceptron, AlexNet, ResNext, SE-ResNet, SE-ResNext). We then propose a Related-key Neural Distinguisher and apply it to the SPECK-/32/64, HIGHT, and GOST block ciphers. The proposed Related-key Neural Distinguisher was constructed using the relationship between keys, and this made it possible to distinguish more rounds than the differential distinguisher.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.175-182
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• 2023

Differential cryptanalysis is one of the analysis techniques for block ciphers, and uses the property that the output difference with respect to the input difference exists with a high probability. If random data and differential data can be distinguished, data complexity for differential cryptanalysis can be reduced. For this, many studies on deep learning-based neural distinguisher have been conducted. In this paper, a deep learning-based neural distinguisher for PIPO 64/128 is proposed. As a result of experiments with various input differences, the 3-round neural distinguisher for the differential characteristics for 0, 1, 3, and 5-rounds achieved accuracies of 0.71, 0.64, 0.62, and 0.64, respectively. This work allows distinguishing attacks for up to 8 rounds when used with the classical distinguisher. Therefore, scalability was achieved by finding a distinguisher that could handle the differential of each round. To improve performance, we plan to apply various neural network structures to construct an optimal neural network, and implement a neural distinguisher that can use related key differential or process multiple input differences simultaneously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3815-3833
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• 2021

MILP-based automatic search is the most common method in analyzing the security of cryptographic algorithms. However, this method brings many issues such as low efficiency due to the large size of the model, and the difficulty in finding the contradiction of the impossible differential distinguisher. To analyze the security of ESF algorithm, this paper introduces a simplified MILP-based search model of the differential distinguisher by reducing constrains of XOR and S-box operations, and variables by combining cyclic shift with its adjacent operations. Also, a new method to find contradictions of the impossible differential distinguisher is proposed by introducing temporary variables, which can avoid wrong and miss selection of contradictions. Based on a 9-round impossible differential distinguisher, 15-round attack of ESF can be achieved by extending forward and backward 3-round in single-key setting. Compared with existing results, the exact lower bound of differential active S-boxes in single-key setting for 10-round ESF are improved. Also, 2108 9-round impossible differential distinguishers in single-key setting and 14 12-round impossible differential distinguishers in related-key setting are obtained. Especially, the round of the discovered impossible differential distinguisher in related-key setting is the highest, and compared with the previous results, this attack achieves the highest round number in single-key setting.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.5
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• pp.87-93
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• 2002

SHACAL is based on the hash standard SHA-1 used in encryption mode, as a submission to NESSIE. SHACAL uses the XOR, modular addition operation and the functions of bit-by-bit manner. These operations and functions make the differential cryptanalysis difficult, i.e, we hardly find a long differential with high probability. But, we can find short differentials with high probability. Using this fact, we discuss the security of SHACAL against the amplified boomerang attack. We find a 36-step boomerang-distinguisher and present attacks on reduced-round SHACAL with various key sizes. We can attack 39-step with 256-bit key, and 47-step with 512-bit key.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.478-493
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• 2024

In this paper, the Mixed Integer Linear Programming (MILP) model is improved for searching differential characteristics of block cipher Midori-64, and 4 search strategies of differential path are given. By using strategy IV, set 1 S-box on the top of the distinguisher to be active, and set 3 S-boxes at the bottom to be active and the difference to be the same, then we obtain a 5-round differential characteristics. Based on the distinguisher, we attack 12-round Midori-64 with data and time complexities of 2⁶³ and 2^103.83, respectively. To our best knowledge, these results are superior to current ones.

• ETRI Journal
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• v.25 no.2
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• pp.89-100
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• 2003

The notion of pseudorandomness is the theoretical foundation on which to consider the soundness of a basic structure used in some block ciphers. We examine the pseudorandomness of the block cipher KASUMI, which will be used in the next-generation cellular phones. First, we prove that the four-round unbalanced MISTY-type transformation is pseudorandom in order to illustrate the pseudorandomness of the inside round function FI of KASUMI under an adaptive distinguisher model. Second, we show that the three-round KASUMI-like structure is not pseudorandom but the four-round KASUMI-like structure is pseudorandom under a non-adaptive distinguisher model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5717-5730
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• 2019

LBlock-s is the core block cipher of authentication encryption algorithm LAC, which uses the same structure of LBlock and an improved key schedule algorithm with better diffusion property. Using the differential properties of the key schedule algorithm and the cryptanalytic technique which combines impossible boomerang attacks with related-key attacks, a 15-round related-key impossible boomerang distinguisher is constructed for the first time. Based on the distinguisher, an attack on 22-round LBlock-s is proposed by adding 4 rounds on the top and 3 rounds at the bottom. The time complexity is about only 2^68.76 22-round encryptions and the data complexity is about 2⁵⁸ chosen plaintexts. Compared with published cryptanalysis results on LBlock-s, there has been a sharp decrease in time complexity and an ideal data complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4727-4741
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• 2019

The lightweight block cipher Piccolo adopts Generalized Feistel Network structure with 64 bits of block size. Its key supports 80 bits or 128 bits, expressed by Piccolo-80 or Piccolo-128, respectively. In this paper, we exploit the security of reduced version of Piccolo from the first round with the pre-whitening layer, which shows the vulnerability of original Piccolo. As a matter of fact, we first study some linear relations among the round subkeys and the properties of linear layer. Based on them, we evaluate the security of Piccolo-80/128 against the meet-in-the-middle attack. Finally, we attack 13 rounds of Piccolo-80 by applying a 5-round distinguisher, which requires $2^{44}$ chosen plaintexts, $2^{67.39}$ encryptions and $2^{64.91}$ blocks, respectively. Moreover, we also attack 17 rounds of Piccolo-128 by using a 7-round distinguisher, which requires $2^{44}$ chosen plaintexts, $2^{126}$ encryptions and $2^{125.49}$ blocks, respectively. Compared with the previous cryptanalytic results, our results are the currently best ones if considering Piccolo from the first round with the pre-whitening layer.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.391-404
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• 2022

Aron Gohr proposed a cryptanalysis method based on deep learning technology for the lightweight block cipher Speck. This is a method that enables a chosen plaintext attack with higher accuracy than the classical differential cryptanalysis. In this paper, by using the probability distribution, we analyze the mechanism of such deep learning based cryptanalysis and propose the results applied to the lightweight block cipher Simon. In addition, we examine that the probability distributions of the predicted values of the neural networks within the cryptanalysis working processes are different depending upon the characteristics of round functions of Speck and Simon, and suggest a direction to improve the efficiency of the neural distinguisher which is the core technology of Aron Gohr's cryptanalysis.