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相关概念视频

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

898
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
898

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混合基因算法和深度学习技术用于高级侧通道攻击.

Faisal Hameed1,2, Hoda Alkhzaimi3,4

  • 1Tandon School of Engineering, New York University, New York, USA. fah276@nyu.edu.

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|July 16, 2025
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概括
此摘要是机器生成的。

本研究介绍了一种基因算法 (GA),用于优化侧通道分析中的深度学习模型. 该GA框架有效地增强了超参数调整,在AES实现中实现了100%的密钥恢复精度.

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科学领域:

  • 密码学 密码学 密码学 密码学
  • 计算机科学 计算机科学
  • 机器学习 机器学习

背景情况:

  • 深度学习已经推进了侧通道分析 (SCA).
  • 超参数优化对于SCA中有效的神经网络训练至关重要.
  • 像网格搜索和贝叶斯优化等传统方法在复杂的搜索空间中有局限性.

研究的目的:

  • 在基于深度学习的SCA中引入基因算法 (GA) 框架,以实现高效的超参数优化.
  • 为了克服网格搜索的可扩展性问题和贝叶斯优化的高维度挑战.
  • 系统地确定最佳的神经网络配置,以提高SCA模型的性能.

主要方法:

  • 开发用于超参数搜索的遗传算法 (GA) 框架.
  • 利用进化策略来导航不可差异的多式联运优化场景.
  • 对保护高级加密标准 (AES) 实现的GA框架的评估.

主要成果:

  • 基于GA的方法在受保护的AES实现上实现了100%的关键恢复精度.
  • 该GA显著超过随机搜索基线 (准确率70%).
  • 与贝叶斯优化,强化学习和树结构的Parzen估计器相比,GA在25%的测试案例中实现了最高性能,并排名第二.

结论:

  • 遗传算法是优化SCA模型的一个强大的替代方案.
  • 在不同的攻击场景中,GA框架提供了可扩展性和一致的性能.
  • 这项研究通过改进的SCA技术推进了对加密安全性的评估.