Multi-path feature enhanced deep learning method for rectangule attack and defense

doi:10.3969/j.issn.1671-1122.2026.07.009

Abstract

Abstract:

To address the issues of single input features and limited capability in identifying cryptographic characteristics when combining traditional rectangle attacks with deep learning, this paper proposed an extended rectangle neural differential distinguisher model, taking the SPECK32/64 algorithm and its variant SPECKEY as the research objects. By constructing ciphertext data groups with triple constraints, the model expanded the unidirectional differential propagation path into multi-path feature inputs, thereby enhancing the neural network’s ability to capture non-random statistical characteristics of high-round ciphers. Experimental results show that the distinguishers for 5 to 8-round SPECK32/64 and 6 to 8-round SPECKEY construct based on this model all outperform the traditional rectangle neural distinguisher in classification accuracy. Furthermore, using the trained 8-round extended rectangle neural differential distinguisher, a subkey recovery attack is performed on the 10-round SPECK32/64 algorithm. The attack results show that while the complete recovery rate for the full 16-bit subkey is 12%, the model achieves a 100% prediction accuracy on the remaining 14 bits after eliminating the influence of two insensitive bits. This study demonstrates the effectiveness of incorporating multi-path feature inputs, providing a more effective analytical model for the application of deep learning in the field of cryptanalysis.

Key words: deep learning, rectangle attack, neural network, key recovery attack

CLC Number:

TP309

Sun Haoran, Chen Jie, Liu Jun. Multi-path feature enhanced deep learning method for rectangule attack and defense[J]. Netinfo Security, 2026, 26(7): 1115-1127.

Figures/Tables 18

References 28

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参数	值
优化器	Adam
损失函数	MSE
正则化参数	$5\times {{10}^{-7}}$
初始学习率	$2.2\times {{10}^{-4}}$
最大学习率	$3.5\times {{10}^{-3}}$
卷积核	$3\times 3$
批量大小/个	5000
训练集样本数/个	${{10}^{7}}$
验证集样本数/个	${{10}^{6}}$
测试集样本数/个	${{10}^{6}}$
训练周期数/轮	30
残差块数/个	10
循环学习率变化周期/轮	10

加密轮数/轮	文献来源	密文对数量/个	训练准确率
5	文献[25]	2 \| 4	97.39% \| 99.14%
	文献[26]	2 \| 4	99.83% \| 99.98%
	文献[27]	2 \| 4	97.80% \| 99.70%
	本文	3	99.97%
6	文献[25]	2 \| 4	86.67% \| 95.38%
	文献[26]	2 \| 4	96.53% \| 98.81%
	文献[27]	2 \| 4	87.70% \| 95.00%
	文献[28]	2 \| 4	87.73% \| 94.97%
	本文	3	98.93%
7	文献[25]	2 \| 4	63.96% \| 68.47%
	文献[26]	2 \| 4	75.37% \| 81.91%
	文献[27]	2 \| 4	66.30% \| 72.50%
	文献[28]	2 \| 4	66.49% \| 72.83%
	本文	3	89.06%
8	文献[26]	2 \| 4	53.19% \| 54.13%
	文献[27]	2 \| 4	52.10% \| 53.00%
	文献[28]	4	54.28%
	本文	3	65.98%

加密轮数/轮	训练准确率
6	99.04%
7	94.27%
8	77.14%

密码算法	加密轮数/轮	准确率	真阳性率	真阴性率
SPECK32/64	5	99.97%	99.96%	99.98%
	6	98.93%	98.61%	99.25%
	7	89.10%	85.42%	92.78%
	8	65.90%	62.99%	68.81%
SPECKEY	6	99.89%	98.11%	99.99%
	7	94.25%	92.12%	96.38%
	8	77.03%	68.23%	85.84%