Security Analysis of LBlock and Its Application Based on Deep Learning

YANG Xiaodong; LI Kaibin; DU Xiaoni; LIANG Lifang; JIA Meichun

doi:10.11999/JEIT221003

Volume 45 Issue 10

Oct. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(10): 3745-3751

YANG Xiaodong, LI Kaibin, DU Xiaoni, LIANG Lifang, JIA Meichun. Security Analysis of LBlock and Its Application Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3745-3751. doi: 10.11999/JEIT221003

Citation:

YANG Xiaodong, LI Kaibin, DU Xiaoni, LIANG Lifang, JIA Meichun. Security Analysis of LBlock and Its Application Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3745-3751. doi: 10.11999/JEIT221003

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Security Analysis of LBlock and Its Application Based on Deep Learning

doi: 10.11999/JEIT221003

YANG Xiaodong^{1, 2, 4},
LI Kaibin^{1, 4},
DU Xiaoni^{2, 3, 4
,
,},
LIANG Lifang^{3, 4},
JIA Meichun^{3, 4}

1.
College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
2.
Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
3.
College of Mathematics and Statistic, Northwest Normal University, Lanzhou 730070, China
4.
Key Laboratory of Cryptography and Data Analytics, Northwest Normal University, Lanzhou 730070, China

Funds: The National Natural Science Foundation of China (62172337), Guangxi Key Laboratory of Cryptography and Information Security (GCI201910)

Received Date: 2022-07-28
Rev Recd Date: 2022-09-02

Available Online: 2022-09-06

Publish Date: 2023-10-31

Abstract

Abstract

Currently, the security analysis of lightweight block ciphers by using deep learning is becoming a new research hotspot. At the Crypto2019, Gohr first applied deep learning to the security analysis of block ciphers, the high-accuracy neural distinguisher is constructed, which used convolutional neural networks to learn the ciphertext distribution of the given input differentials. LBlock is a lightweight block cipher with excellent software and hardware implementation efficiency, which attracted extensive attention from scholars since its publication. In this paper, with the application of the residual network, a round-reduced neural differential distinguisher of LBlock is constructed, in which the accuracy of the 7-round and 8-round distinguishers reach 0.999 and 0.946, respectively. Moreover, based on the 9-round neural distinguisher, a key recovery attack scheme against 11-round LBlock is proposed. Experiment results show that under the case of the number of iteration rounds of the algorithm is small, the scheme need not consider the S-box separately. Compared with the traditional attack schemes, the new scheme is not only simpler and easy to be implemented, but also possess great advantages on data complexity and time complexity.
- LBlock,
- Differential distinguisher,
- Deep learning,
- Key recovery attack

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References(17)

References

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