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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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相关实验视频

Updated: Jul 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Published on: December 15, 2023

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在网络流量中使用深度学习算法检测分布式拒绝服务攻击.

Mahrukh Ramzan1, Muhammad Shoaib1, Ayesha Altaf1

  • 1Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
概括
此摘要是机器生成的。

像GRU,LSTM和RNN这样的深度学习模型可以有效地检测分布式拒绝服务 (DDoS) 攻击. 格鲁提供更快的检测时间,增强互联网安全解决方案.

关键词:
深度学习是一种深度学习.拒绝服务攻击检测攻击检测分布式拒绝服务攻击.网络安全 网络安全

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 随着越来越多的IT和云计算采用,互联网安全问题越来越关注.
  • 传统的安全措施与复杂的拒绝服务 (DoS) 和分布式拒绝服务 (DDoS) 攻击作斗争.
  • 深度学习为检测复杂网络流量攻击提供了先进的功能.

研究的目的:

  • 评估用于检测DDoS攻击的深度学习模型.
  • 为了比较循环神经网络 (RNN),长期短期记忆 (LSTM) 和梯度循环单元 (GRU) 模型的性能.
  • 分析最近数据集的检测准确度和执行时间.

主要方法:

  • 使用的深度学习模型:RNN,LSTM和GRU.
  • 在CICDDoS2019和CICIDS2017数据集上测试模型.
  • 对模型性能进行了比较分析,重点关注准确性和执行时间.

主要成果:

  • 所有评估的深度学习模型都在CICDDoS2019数据集上实现了高精度 (0.99).
  • 与RNN和LSTM相比,渐变循环单元 (GRU) 的执行时间显著减少.
  • 对比分析证实了深度学习对DDoS检测的有效性.

结论:

  • 深度学习模型,特别是GRU,为DDoS攻击检测提供了准确和高效的解决方案.
  • 该研究提供了一种有效的方法来增强网络安全,防止复杂的攻击.
  • 调查结果表明,GRU是DDoS检测的首选模型,因为它的速度和准确性.