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相关概念视频

Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

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The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
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Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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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...
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2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

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Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
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混合量子增强联合学习用于网络攻击检测和检测.

G Subramanian1, M Chinnadurai2

  • 1Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. g.subramanian190@gmail.com.

Scientific reports
|December 31, 2024
PubMed
概括

这项研究引入了一种新的联合学习方法,用于检测网络攻击,增强网络安全. 该方法提高了异常检测准确度和隐私保护,优于传统模型.

科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 传统的网络攻击检测方法面临着数据隐私和可扩展性的挑战.
  • 集中式系统难以快速适应不断变化的网络威胁.
  • 传统方法的局限性需要创新的,分散的解决方案.

研究的目的:

  • 开发一种新的联合学习解决方案,用于增强网络攻击检测.
  • 解决关于数据隐私和通信开销的集中方法的局限性.
  • 提高网络安全系统的适应性和性能.

主要方法:

  • 整合一个时空注意力网络 (STAN) 用于模式识别.
  • 实施量子启发的联合平均化 (QIFA) 优化程序.
  • 使用层次模型聚合和多阶段的精细化,同时保护隐私.

主要成果:

  • 拟议的模型实现了高性能指标:98.2%的精度,98.5%的回忆,98.35%的F1得分,98.2%的特异性和98.34%的准确性.
  • 与传统的CNN,LSTM,RNN和基线联合学习模型相比,表现出卓越的性能.
  • 使用UNSW-NB15数据集有效检测各种网络异常.
关键词:
网络攻击 网络攻击联合学习是联合学习.优化优化 优化优化量子原理是一个量子原理.时间空间网络 时间空间网络

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结论:

  • 新的联合学习方法显著提高了网络攻击检测能力.
  • 整合STAN和QIFA提供了一个强大且保护隐私的解决方案.
  • 拟议的模型代表了适应性和高效网络安全的重大进步.