• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.477-489
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• 2019

In this paper, we experimentally verify the expected differential probability under the markov cipher assumption and the distribution of the differential probability. Firstly, we validate the expected differential probability of 6round-PRESENT of the lightweight block cipher under the markov cipher assumption by analyzing the empirical differential probability. Secondly, we demonstrate that even though the expected differential probability under the markov cipher assumption seems valid, the empirical distribution does not follow the well-known distribution of the differential probability. The results was deduced from the 4round-GIFT. Finally, in order to analyze whether the key-schedule affects the mis-matching phenomenon, we collect the results while changing the XOR positions of round keys on GIFT. The results show that the key-schedule is not the only factor to affect the mis-matching phenomenon. Leveraging on GPGPU technology, the data collection process can be performed about 157 times faster than using CPU only.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.600-616
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• 2021

SIMON and SPECK are two families of lightweight block ciphers that have excellent performance on hardware and software platforms. At CRYPTO 2019, Gohr first introduces the differential cryptanalysis based deep learning on round-reduced SPECK32/64, and finally reduces the remaining security of 11-round SPECK32/64 to roughly 38 bits. In this paper, we are committed to evaluating the safety of SIMON cipher under the neural differential cryptanalysis. We firstly prove theoretically that SIMON is a non-Markov cipher, which means that the results based on conventional differential cryptanalysis may be inaccurate. Then we train a residual neural network to get the 7-, 8-, 9-round neural distinguishers for SIMON32/64. To prove the effectiveness for our distinguishers, we perform the distinguishing attack and key-recovery attack against 15-round SIMON32/64. The results show that the real ciphertexts can be distinguished from random ciphertexts with a probability close to 1 only by 2^8.7 chosen-plaintext pairs. For the key-recovery attack, the correct key was recovered with a success rate of 23%, and the data complexity and computation complexity are as low as 2⁸ and 2^20.1 respectively. All the results are better than the existing literature. Furthermore, we briefly discussed the effect of different residual network structures on the training results of neural distinguishers. It is hoped that our findings will provide some reference for future research.