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相关概念视频

Naturalistic Observations02:30

Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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在物联网中使用中断感知匿名用户系统检测方法 (IAU-S-DM) 分析匿名活动.

Hani Alshahrani1,2, Mohd Anjum3, Sana Shahab4

  • 1Department Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia.

Scientific reports
|August 5, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了中断意识的匿名用户系统检测方法 (IAU-S-DM),以有效地检测物联网 (IoT) 网络中的入侵者. 这种新的方法显著减少了精确的入侵检测计算时间.

关键词:
反对的分类是不利的分类.深度循环学习 (deep recurrent learning) 是一种经常性学习.物联网的物联网,就是物联网.侵入检测系统的入侵检测系统服务故障服务故障

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相关实验视频

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科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

  • 对物联网 (IoT) 网络的未经授权访问构成重大安全风险.
  • 现有的入侵检测系统经常受到高计算时间的影响,阻碍了准确和及时的入侵者识别.
  • 在物联网数据传输期间的中间访问引入了漏洞.

研究的目的:

  • 为物联网网络开发一种高效的入侵检测方法.
  • 为应对现有系统中高计算时间的挑战.
  • 提高在物联网环境中识别未经授权访问的准确性和速度.

主要方法:

  • 实施中断感知匿名用户系统检测方法 (IAU-S-DM).
  • 使用隐藏服务会话来检测匿名中断.
  • 用参数训练系统,包括来源,会话访问需求和用户合法性.
  • 在数据处理中采用深度循环学习方法,以识别服务故障和漏洞.

主要成果:

  • 根据IAU-S-DM方法,服务故障率为10.65%.
  • 实现了14.63%的检测精度和15.54%的检测时间改进.
  • 分类比达到20.51%,表明有效的入侵者活动识别.

结论:

  • IAU-S-DM方法提供了一个计算效率高的解决方案,用于检测物联网网络中的入侵.
  • 深度循环学习提高了服务故障和漏洞的识别,提高了检测率.
  • 该方法有效地利用TON-IoT数据集来识别入侵者活动并验证系统的一致性.