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An Improved Differential Fault Analysis on Block Cipher KLEIN-64

Min Long1, *, Man Kong1, Sai Long1, Xiang Zhang2

1 Changsha University of Science and Technology, Changsha, 410014, China.
2 School of Computing, National University of Singapore, Singapore.

* Corresponding Author: Min Long. Email: email.

Computers, Materials & Continua 2020, 65(2), 1425-1436. https://doi.org/10.32604/cmc.2020.011116

Abstract

KLEIN-64 is a lightweight block cipher designed for resource-constrained environment, and it has advantages in software performance and hardware implementation. Recent investigation shows that KLEIN-64 is vulnerable to differential fault attack (DFA). In this paper, an improved DFA is performed to KLEIN-64. It is found that the differential propagation path and the distribution of the S-box can be fully utilized to distinguish the correct and wrong keys when a half-byte fault is injected in the 10th round. By analyzing the difference matrix before the last round of S-box, the location of fault injection can be limited to a small range. Thus, this improved analysis can greatly improve the attack efficiency. For the best case, the scale of brute-force attack is only 256. While for the worst case, the scale of brute-force attack is far less than 232 with another half byte fault injection, and the probability for this case is 1/64. Furthermore, the measures for KLEIN-64 in resisting the improved DFA are proposed.

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Cite This Article

APA Style
Long, M., Kong, M., Long, S., Zhang, X. (2020). An improved differential fault analysis on block cipher KLEIN-64. Computers, Materials & Continua, 65(2), 1425-1436. https://doi.org/10.32604/cmc.2020.011116
Vancouver Style
Long M, Kong M, Long S, Zhang X. An improved differential fault analysis on block cipher KLEIN-64. Comput Mater Contin. 2020;65(2):1425-1436 https://doi.org/10.32604/cmc.2020.011116
IEEE Style
M. Long, M. Kong, S. Long, and X. Zhang, “An Improved Differential Fault Analysis on Block Cipher KLEIN-64,” Comput. Mater. Contin., vol. 65, no. 2, pp. 1425-1436, 2020. https://doi.org/10.32604/cmc.2020.011116

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cc Copyright © 2020 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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