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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

156
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
156
Types Of Transformers01:16

Types Of Transformers

1.0K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.0K
Classification of Systems-I01:26

Classification of Systems-I

301
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:
301
Transformers01:26

Transformers

1.2K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.2K
Classification of Systems-II01:31

Classification of Systems-II

241
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
241

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相关实验视频

Updated: Sep 11, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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在物联网网络中使用深度学习和变压器进行DDoS攻击的多类入侵检测系统.

Sheikh Abdul Wahab1,2, Saira Sultana1, Noshina Tariq3

  • 1Department of Computing and Technology, H-9 Campus, Iqra University, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种深度学习 (DL) 入侵检测系统 (IDS),用于打击物联网 (IoT) 网络中的分布式拒绝服务 (DDoS) 攻击. 基于DL的IDS有效检测各种DDoS威胁,增强物联网安全.

关键词:
卷积神经网络是一个卷积神经网络.深度学习 (Deep Learning) 是一种深度学习.分布式拒绝服务.物联网的安全性 物联网的安全性侵入检测系统 侵入检测系统合成少数群体过量采样技术变压器变压器变压器检测异常检测异常检测

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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:

  • 物联网 (IoT) 设备的扩散导致对分布式拒绝服务 (DDoS) 攻击的脆弱性增加.
  • 物联网网络中的DDoS攻击会破坏通信,损害服务可用性,并造成重大运营和经济损失.

研究的目的:

  • 开发和评估基于深度学习 (DL) 的入侵检测系统 (IDS),专门为物联网环境设计.
  • 评估卷积神经网络 (CNN),深度神经网络 (DNN) 和变压器模型在检测物联网网络中各种类型的DDoS攻击中的有效性.

主要方法:

  • 实现了三个DL架构:CNN,DNN和变压器模型用于入侵检测.
  • 利用CiC IoT 2023数据集进行IDS模型的培训和测试.
  • 采用数据预处理技术,包括日志规范化和基于SMOTE的过量采样,以处理特征分布和类不平衡.

主要成果:

  • 在二进制分类 (DNN: 99.2%,CNN: 99.0%,变压器: 98.8%) 中实现了高精度,用于检测DDoS攻击.
  • 在三类分类 (良性,DDoS,非DDoS) 中表现出近乎完美的性能 (99.9-100%).
  • 在12个类别的分类中达到很高的准确性 (DNN:93.0%,CNN:92.7%,变压器:92.5%),包括良性流量和12种攻击类型.

结论:

  • 拟议的基于DL的IDS显示了物联网DDoS检测的高效率,精度,回忆和ROC-AUC值.
  • 该系统在检测准确性和效率方面优于现有的最先进方法,提供了可扩展的解决方案.
  • 集成到IDS框架中的高级DL模型为物联网网络中不断发展的DDoS威胁提供了强大的防御.