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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Distributed Loads01:19

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
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相关实验视频

Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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使用属性子集选择与时间卷积网络缓解分布式拒绝服务攻击.

Hayam Alamro1, Asmaa Mansour Alghamdi2, Asma Alshuhail3

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

Scientific reports
|December 2, 2025
PubMed
概括

本研究介绍了一种使用Salp Swarm-Based Feature Selection和深度学习架构 (IFAD-SSFSDLA) 进行攻击检测的新智能框架,以打击分布式拒绝服务 (DDoS) 攻击. 这种新型模型在实时DDoS攻击检测中实现了高精度.

关键词:
DDoS攻击检测检测 DDoS攻击检测数据预处理数据的预处理.深度学习是一种深度学习.萨尔普群群算法 萨尔普群群算法 萨尔普群群算法时间卷积网络 时间卷积网络

相关实验视频

Last Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

996

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:

  • 分布式拒绝服务 (DDoS) 攻击对网络基础设施和服务构成重大和不断变化的威胁.
  • 现有的方法与现代DDoS攻击的动态模式和复杂性作斗争,需要先进的检测技术.
  • 实时识别和缓解DDoS威胁对于防止服务中断和数据泄露至关重要.

研究的目的:

  • 提出一种新的智能攻击检测框架,使用Salp Swarm-Based Feature Selection和深度学习架构 (IFAD-SSFSDLA) 进行实时DDoS攻击检测.
  • 通过优化功能选择和深度学习,提高DDoS攻击检测的准确性和效率.
  • 为识别和减轻日益复杂的DDoS攻击的影响提供强大的解决方案.

主要方法:

  • 使用min-max规范化进行数据预处理,用于清理和结构化原始网络流量数据.
  • 使用Salp Swarm算法 (SSA) 进行特征选择,以识别和保留最具歧视性的特征,以提高模型性能.
  • 使用时间卷积网络 (TCN) 深度学习架构进行攻击分类.

主要成果:

  • 国际开发基金-SSFSDLA模型在检测DDoS攻击方面表现出卓越的性能.
  • 在CIC-IDS-2017数据集上达到99.56%的高准确率,在Edge-IIoT数据集上达到99.65%.
  • 在多个数据集的DDoS攻击检测准确性方面超越现有技术.

结论:

  • 拟议的IFAD-SSFSDLA模型为实时DDoS攻击检测提供了一个有效和准确的解决方案.
  • 整合Salp Swarm算法用于特征选择和时间卷积网络用于分类,显著提高了检测能力.
  • 这一框架在网络安全方面提供了有价值的进步,用于打击普遍和不断变化的网络威胁.