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相关概念视频

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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相关实验视频

Updated: Jun 1, 2025

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
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使用WGAN-GP和遗传算法优化加密协议,以防止使用WGAN-GP和遗传算法进行侧通道攻击.

Purushottam Singh1, Prashant Pranav2, Sandip Dutta2

  • 1Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India. purushottamsingh@outlook.com.

Scientific reports
|January 17, 2025
PubMed
概括

本研究介绍了使用机器学习和遗传算法来增强安全性和效率的混合加密框架. AES-GCM表现出卓越的性能,展示了网络安全协议的进步.

关键词:
密码学协议 密码学协议数据增强数据增强遗传算法 遗传算法 遗传算法安全优化安全优化侧通道攻击是侧通道攻击.在WGAN-GP中使用.

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科学领域:

  • 网络安全 网络安全
  • 密码学 密码学 密码学 密码学
  • 机器学习 机器学习

背景情况:

  • 传统的加密协议面临着不断变化的安全威胁.
  • 提高对加密分析的效率和抵抗力对于现代网络安全至关重要.

研究的目的:

  • 引入一个新的混合加密框架.
  • 评估先进方法论对加密协议的影响.
  • 评估安全性,效率和对攻击的抵抗力.

主要方法:

  • 瓦斯斯坦生成对抗网络与梯度惩罚 (WGAN-GP) 和遗传算法 (GA) 的集成.
  • 对加密协议的评估:AES-ECB,AES-GCM,ChaCha20,RSA,以及ECC. 这些都是加密协议.
  • 分析安全级别,效率,侧通道阻力和加密分析阻力.

主要成果:

  • 混合框架显著提高了所有测试的协议的安全性和效率.
  • 在最短的计算时间内,AES-GCM表现出卓越的性能.
  • 通过综合方法观察到强大的侧通道阻力.

结论:

  • 机器学习和进化算法可以显著提高加密协议的安全性和效率.
  • 开发的框架为未来的网络安全创新提供了坚实的基础.
  • 混合方法为下一代加密解决方案提供了一个有希望的方向.