Lightweight block cipher algorithm identification scheme based on transformer

Ke Yuan

doi:10.1007/s12530-026-09802-4

2026-02-23

PRE

DOI：10.1007/s12530-026-09802-4

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摘要

En 中文

当前对加密算法识别的研究在分组密码领域已逐渐成熟，但在轻量级分组密码方面刚刚起步，研究成果较少，且影响加密算法识别结果的因素过于单一；同时，传统机器学习算法无法有效处理包含大量数据和结构信息的密文，导致识别率受限。提出了基于Transformer的轻量级分组密码算法单层和多层识别方案，并改进了NIST随机性测试和熵测试中的密文特征提取方法。单层识别方案将四种轻量级分组密码算法KASUMI、SIMON、CLEFIA和PRESENT进行二分识别。实验表明，相较于三种传统机器学习方法，该方案在使用固定密钥加密时，平均识别率提升超过30%；在使用随机密钥加密时，相比传统机器学习方法也有显著改进。多层识别方案利用Transformer模型首先对六种轻量级分组密码算法KASUMI、SKINNY、CLEFIA、PRESENT、SIMON和Midori进行S-F聚类分类（SP-Feistel结构），然后在同一结构聚类下进行单分类。实验显示，与单层Transformer进行六类识别的识别率54.2%相比，该方案具有更优的准确性和稳定性。

Keyword:

Cryptographic algorithm identification

Lightweight block cipher

Deep learning

Transformer

Feature extraction

期刊

Evolving Systems

IF:

2.7

论文数:

244

被引数:

1.2K

机构

computer and information engineering

学者数:

174

论文数: 69

被引数: 0

college of cryptology and cyberscience

学者数:

论文数: 1

被引数: 0

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