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相关实验视频

Updated: Jan 16, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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可解释的基于人工智能的物联网网络网络网络的网络弹性,使用混合深度学习与改进的黑猩猩优化算法.

Sarah A Alzakari1, Mohammed Aljebreen2, Nazir Ahmad3

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Scientific reports
|September 26, 2025
PubMed
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Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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本研究介绍了一种使用混合深度学习和优化来解释网络弹性的人工智能 (XAICR) 方法. 它显著提高了物联网 (IoT) 环境中的网络威胁检测和解释.

科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 物联网的物联网,就是物联网.

背景情况:

  • 物联网 (IoT) 设备的扩散需要先进的异常检测方法.
  • 现有的基于AI和机器学习 (ML) 的物联网入侵检测系统 (IDS) 面临着透明度和有限的攻击数据的挑战.
  • 网络安全缺乏透明度,阻碍了对关键决策和相关风险的明确解释.

研究的目的:

  • 通过使用混合深度学习和优化算法 (XAICR-HDLOA) 方法,为网络弹性提供可解释的人工智能.
  • 增强网络威胁的检测和解释,特别是在物联网环境中.
  • 通过增强模型解释性来提高网络安全的信任和可靠性.

主要方法:

  • 使用min-max规范化进行数据预处理.
  • 通过白搜索 (BES) 模型选择功能.
  • 使用混合卷积神经网络-双向门循环单元 (CNN-BiGRU) 模型进行网络攻击分类.
  • 使用改进的黑猩猩优化算法 (IChoA) 进行超参数调整.
  • 使用SHAP (夏普利添加式解释) 的模型解释性增强.

主要成果:

  • 在XAICR-HDLOA方法实现了高准确率的98.41%在边缘-IIoT数据集和98.25%在BoT-IoT数据集.
关键词:
网络安全 网络安全数据规范化的数据规范化.深度学习是一种深度学习.缩小尺寸的缩小方式可解释的人工智能物联网的物联网,就是物联网.

相关实验视频

Last Updated: Jan 16, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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  • 与网络威胁检测和解释的现有方法相比,表现出优越的性能.
  • 成功提高了模型的解释性,促进了更大的信任和可靠性.

    • 结论:

    • 拟议的XAICR-HDLOA方法有效地解决了物联网当前基于AI/ML的IDS的局限性.
    • 可解释的人工智能技术对更透明和可靠的网络安全解决方案做出了重大贡献.
    • 该方法为相互连接的环境中强大的网络弹性提供了一个有希望的方向.