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Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm
Applied Sciences ( IF 2.5 ) Pub Date : 2021-01-18 , DOI: 10.3390/app11020839
Shaofei Sun , Hongxin Zhang , Xiaotong Cui , Liang Dong , Muhammad Saad Khan , Xing Fang

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.

中文翻译:

基于粒子群算法的多字节电磁分析

本文重点讨论通信系统中的电磁信息安全性。经典相关电磁分析(CEMA)是恢复密码算法密钥的一种有效方法。在传统方法中,仅使用密钥的一个字节,而将其他字节视为噪声,这不仅降低了效率,而且浪费了信息。为了充分利用有用的信息,使用了密钥的多个字节。我们将密钥转换为多维形式,并将密钥的每个字节视为一个维度。正确的关键字搜索问题转化为优化关键字候选者相关系数的问题。粒子群算法(PSO)特别适合解决高维,复杂结构的优化问题。在本文中,我们将PSO算法应用到CEMA中以解决多维问题,并且还向优化算法中添加了一个变异算子以改善结果。在这里,我们提出了基于粒子群优化的多字节相关电磁分析。我们在旨在测试和开发硬件安全性的通用测试板上验证了我们的方法。我们在测试板上实施了高级加密标准(AES)加密算法。实验结果表明,我们的方法优于经典方法。对于相应的案例,它可实现约13.72%的改进。我们将PSO算法应用于CEMA以解决多维问题,并且还向优化算法中添加了一个变异算子以改善结果。在这里,我们提出了基于粒子群优化的多字节相关电磁分析。我们在旨在测试和开发硬件安全性的通用测试板上验证了我们的方法。我们在测试板上实施了高级加密标准(AES)加密算法。实验结果表明,我们的方法优于经典方法。对于相应的案例,它可实现约13.72%的改进。我们将PSO算法应用于CEMA以解决多维问题,并且还向优化算法中添加了一个变异算子以改善结果。在这里,我们提出了基于粒子群优化的多字节相关电磁分析。我们在旨在测试和开发硬件安全性的通用测试板上验证了我们的方法。我们在测试板上实施了高级加密标准(AES)加密算法。实验结果表明,我们的方法优于经典方法。对于相应的案例,它可实现约13.72%的改进。我们提出了一种基于粒子群算法的多字节相关电磁分析。我们在旨在测试和开发硬件安全性的通用测试板上验证了我们的方法。我们在测试板上实施了高级加密标准(AES)加密算法。实验结果表明,我们的方法优于经典方法。对于相应的案例,它可实现约13.72%的改进。我们提出了一种基于粒子群算法的多字节相关电磁分析。我们在旨在测试和开发硬件安全性的通用测试板上验证了我们的方法。我们在测试板上实施了高级加密标准(AES)加密算法。实验结果表明,我们的方法优于经典方法。对于相应的案例,它可实现约13.72%的改进。
更新日期:2021-01-18
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