Linear approximation attacks on reduced-round block ciphers
*Swapnil SonawaneCorresponding authorswapnil.Sonawane@vit.edu.inDepartment of Computer EngineeringWadalaVidyalankar Institute of TechnologyMumbai, Maharashtra, IndiaView full profile → , Dilip Motwanidilip.motwani@vit.edu.inDepartment of Information TechnologyVidyalankar Institute of TechnologyMumbai, Maharashtra, 400037, IndiaView full profile → , Vidya Chitrevidya.chitre@vit.edu.inDepartment of Information TechnologyVidyalankar Institute of TechnologyMumbai, Maharashtra, 400037, IndiaView full profile → , Amit Nerurkaramit.nerurkar@vit.edu.inDepartment of Computer EngineeringVidyalankar Institute of TechnologyMumbai, Maharashtra, IndiaView full profile → , Varsha Bhosalevarsha.bhosale@vit.edu.inDepartment of Information TechnologyVidyalankar Institute of TechnologyMumbai, Maharashtra, 400037, IndiaView full profile → , Rugved Deolekarrugved.deolekar@vit.edu.inDepartment of Information TechnologyVidyalankar Institute of TechnologyMumbai, Maharashtra, 400037, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3037
- Pages:
- 4121–4131
Abstract
Linear cryptanalysis is still a key method to analyze the security of symmetric-key primitives, especially reduced-round block ciphers, in which structural weaknesses are more revealed. This paper examines linear approximation attacks on reduced-round variants of substitution permutation networks (SPNs) and Feistel ciphers, including finding high bias linear expressions in S-box layers and key mixing. The approach combines the linear hull analysis and the statistical hypothesis testing to accumulate the bias during several rounds, thus providing a way to recover keys. Experimental analysis shows that reduced-round designs have quantifiable biases up to 0.125, and achieve successful key recovery probabilities above 92.4 percent with much less plaintext-ciphertext pairs than designs using full rounds.
Keywords
Subject Classifications
References
[1] R. Almukhlifi and P. L. Vora, “Linear cryptanalysis of reduced-round Simeck using super rounds,” Cryptography, vol. 7, no. 1, Art. no. 8 (2023), doi: 10.3390/cryptography7010008.
[2] S. A. Alzakari and P. L. Vora, “Partly-pseudo-linear cryptanalysis of reduced-round Speck,” Cryptography, vol. 5, no. 1, Art. no. 1 (2021), doi: 10.3390/cryptography5010001.
[3] D. Roh, “Differential–linear approximations of CHAM,” Electronics, vol. 13, no. 16, Art. no. 3141 (2024), doi: 10.3390/electronics13163141.
[4] K. D. Muthavhine and M. Sumbwanyambe, “Blocking linear cryptanalysis attacks found on cryptographic algorithms used on Internet of Thing based on the novel approaches of using Galois field (GF (232)) and high irreducible polynomials,” Applied Sciences, vol. 13, no. 23, Art. no. 12834 (2023), doi: 10.3390/app132312834.
[5] L. Zhang, Y. Wu, Y. Wen, C. Xiao, D. Ding, and Q. Lv, “Differential cryptanalysis of block ciphers through the lens of symmetry: A review,” Symmetry, vol. 18, no. 1, Art. no. 8 (2026), doi: 10.3390/sym18010008.
[6] S. M. Dehnavi, “Further observations on SIMON and SPECK block cipher families,” Cryptography, vol. 3, no. 1, Art. no. 1 (2019), doi: 10.3390/cryptography3010001.
[7] S. Limkar, W. V. Ashok, S. Singh, S. Singh, S. K. Wagh, and S. N. Ajani, “A mechanism to ensure identity-based anonymity and authentication for IoT infrastructure using cryptography,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 5, pp. 1597–1611 (2023).
[8] O. Jeong, S. Park, and I. Moon, “Scalable neural cryptanalysis of block ciphers in federated attack environments,” Mathematics, vol. 14, no. 2, Art. no. 373 (2026), doi: 10.3390/math14020373.
[9] Q. Naaz, M. R. Beg, H. R. Khan, and H. U. Rahman, “ExecuChat: A secure AI-integrated web-based chat application with code debugging and execution,” International Journal on Advanced Computer Theory and Engineering, vol. 14, no. 1, pp. 214–218 (2025), doi: 10.65521/ijacte.v14i1.393.
[10] J. Dani, K. Nakka, and N. Saxena, “MIND-Crypt: A machine learning framework for assessing the indistinguishability of lightweight block ciphers across multiple modes of operation,” Cryptography, vol. 10, no. 1, Art. no. 9 (2026), doi: 10.3390/cryptography10010009.
[11] K. Muthavhine and M. Sumbwanyambe, “Securing IoT devices against differential-linear (DL) attack used on Serpent algorithm,” Future Internet, vol. 14, no. 2, Art. no. 55 (2022), doi: 10.3390/fi14020055.
[12] O. Jeong, E. Ahmadzadeh, and I. Moon, “Comprehensive neural cryptanalysis on block ciphers using different encryption methods,” Mathematics, vol. 12, no. 13, Art. no. 1936 (2024), doi: 10.3390/math12131936.
[13] R. C. Poonia, R. Almakki, A. K. J. Saudagar, A. Altameem, and M. Albathan, “An optimization approach for real-time object detection in IoT devices through edge computing and deep learning,” Journal of Information and Optimization Sciences, vol. 45, no. 5, pp. 1465–1475 (2024), doi: 10.47974/JIOS-1768.




