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相关概念视频

False Memories01:18

False Memories

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False memories represent a cognitive distortion in which individuals recall events that did not happen, or remember them in an altered form. This phenomenon highlights the brain's constructive nature in processing and recalling memories, emphasizing that memory is not a perfect representation of past events but rather a dynamic reconstruction influenced by various factors.
One primary source of false memories is misattribution, where individuals incorrectly associate external information...
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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相关实验视频

Updated: May 29, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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通过使用深度学习模型来减少属性来增强网络安全,以识别假数据注入攻击和识别假数据注入攻击.

Faheed A F Alrslani1, Manal Abdullah Alohali2, Mohammed Aljebreen3

  • 1Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia.

Scientific reports
|January 31, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于深度学习的虚假数据注入攻击识别 (ARDL-FDIAR) 技术的新型属性减少技术,以提高电力系统的安全性. 该ARDL-FDIAR方法有效地检测和减轻虚假数据注入攻击,提高电网弹性.

关键词:
鱼优化算法 鱼优化算法网络攻击 网络攻击深度信念网络深度信念网络深度学习是一种深度学习.虚假数据注入攻击

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 网络安全 网络安全

背景情况:

  • 假数据注入攻击 (FDIA) 通过绕过传统安全措施,对电力系统运行构成重大威胁.
  • 可再生能源的日益集成需要实时监控和强大的网络稳定性安全.
  • 对于电网运行至关重要的状态估计算法在FDIA期间容易受到恶意数据操纵的影响.

研究的目的:

  • 在电力系统中引入一种用于识别虚假数据注入攻击 (FDIA) 的新技术.
  • 增强电网对复杂的网络威胁的安全性和弹性.
  • 通过先进的深度学习方法提高FDIA检测的准确性和效率.

主要方法:

  • 开发了基于深度学习的虚假数据注入攻击识别 (ARDL-FDIAR) 技术的属性减少.
  • 使用Z-score规范化进行数据缩放和修改的Lemrus优化算法 (MLOA) 进行特征选择.
  • 采用了改进的深信网络 (IDBN) 模型来检测FDIA,其超参数由鱼优化算法 (COA) 调整.

主要成果:

  • 在检测FDIA时,ARDL-FDIAR技术显示了增强的安全性能.
  • 实验结果证实了与现有的深度学习方法相比,拟议的方法的有效性.
  • 该研究验证了IDBN模型的改进准确性和可靠性,并使用基于COA的超参数调整.

结论:

  • ARDL-FDIAR技术为电力系统中的实时FDIA检测提供了一个强大的解决方案.
  • 拟议的方法显著提高了电网安全性和运营弹性.
  • 这项研究有助于保护智能电网免受新出现的网络威胁.