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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

742
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
742
Language and Cognition01:27

Language and Cognition

881
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
881

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相关实验视频

Updated: May 3, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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面向IIoT的混合入侵检测框架使用大型语言模型

Musaad Algarni1, Mohamed Y Dahab1, Abdulaziz A Alsulami2

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于工业物联网 (IIoT) 安全的新型入侵检测系统 (IDS). 混合框架通过结合文本和数字数据来增强网络安全,实现高精度并防止数据泄露.

关键词:
事物的工业互联网 (IIoT)大型语言模型 (LLM)主要组成部分分析 (PCA)的分类 (CLS) 进行.侵入检测系统 (IDS) 是一种入侵检测系统.

相关实验视频

Last Updated: May 3, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

科学领域:

  • 网络安全 网络安全
  • 事物的工业互联网 (IIoT)
  • 机器学习 机器学习

背景情况:

  • 工业物联网 (IIoT) 连接提高了效率,但引入了重大网络安全风险.
  • 现有的入侵检测系统 (IDS) 面临着IIoT的挑战,原因是数据异质性,高维度,类不平衡和数据泄露.
  • 有效的IDS对于保护IIoT环境免受不断变化的网络威胁至关重要.

研究的目的:

  • 为工业物联网 (IIoT) 环境提出一个漏洞安全的混合入侵检测框架.
  • 整合基于文本和数值的网络流特征,以进行强大的威胁检测.
  • 为了应对数据异质性,高维度和IIoT安全中的类不平衡等挑战.

主要方法:

  • 网络流转换为文本描述,并使用转换器的双向编码器表示 (BERT) 来编码语义嵌入.
  • 数字流量特征标准化并与LLM嵌入相结合.
  • 在主要组件分析 (PCA) 空间中计算的类原型,添加了等号相似性得分.
  • 合成少数群体过量采样技术 (SMOTE) 用于类不平衡,随机森林 (RF) 用于特征选择,以及基于直方图的梯度增强 (HGB) 用于分类.

主要成果:

  • 混合框架在Edge-IIoTset数据集上实现了98.19%的准确性.
  • 该框架在ToN_IoT数据集上显示了99.15%的准确性.
  • 拟议的方法在评估 IIoT 网络流量时被证明是稳健且无泄漏的.

结论:

  • 开发的防泄漏混合IDS框架有效地检测到IIoT环境中的入侵.
  • 将基于LLM的文本嵌入与数字特征相结合,可以增强检测能力.
  • 该框架为保护相互连接的工业系统免受网络威胁提供了一个有希望的解决方案.