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相关概念视频

Cable Subjected to a Distributed Load01:24

Cable Subjected to a Distributed Load

723
The analysis of suspension bridges is a complex and critical process that involves multiple factors, including the shape and tension of the main cables. The main cables of suspension bridges are subjected to distributed loads, which result in changes in tensile forces and deformation of the cable. These loads must be carefully considered to ensure that the bridge is safe and capable of supporting the weight of different loads.
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相关实验视频

Updated: Jul 19, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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高速网络DDoS攻击检测:一项调查

Rana M Abdul Haseeb-Ur-Rehman1, Azana Hafizah Mohd Aman1, Mohammad Kamrul Hasan1

  • 1Center for Cyber Security, Faculty of Information Science and Technology, University Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
概括
此摘要是机器生成的。

在高速网络 (HSN) 上检测分布式拒绝服务 (DDoS) 攻击是具有挑战性的. 本文审查了用于提高网络安全和网络物理系统 (CPS) 完整性的机器学习方法.

关键词:
网络物理系统拒绝提供服务.分布式拒绝服务.快速表达数据路径数据路径高速网络高速网络.侵入检测系统的入侵检测系统机器学习是机器学习.

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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
  • 网络工程 网络工程

背景情况:

  • 大量的设备连接会造成网络漏洞,增加分布式拒绝服务 (DDoS) 攻击的风险.
  • DDoS攻击可能会导致大量的财务损失和数据损坏,损害网络物理系统 (CPS) 的完整性.
  • 目前的DDoS检测技术往往是无效的,特别是对于高速网络 (HSN),由于快速的数据包处理.

研究的目的:

  • 审查和比较各种机器学习 (ML) 方法来检测DDoS攻击.
  • 分析ML技术的有效性,如k-means,K-Nearest Neighbors (KNN) 和Naive Bayes (NB) 在入侵检测系统 (IDS) 和基于流量的IDS中.
  • 识别挑战,并建议未来的研究方向,在HSN中检测DDoS攻击.

主要方法:

  • 对有关检测不规则交通模式的现有文献进行定性分析.
  • 在入侵检测系统 (IDS) 和基于流程的IDS中检查机器学习技术 (k-means,KNN,NB).
  • 审查数据路径的数据包过及其对HSN性能的影响.

主要成果:

  • 目前的DDoS检测方法与高速网络的复杂性作斗争.
  • 机器学习技术显示了提高DDoS检测准确性的潜力.
  • 详细的DDoS攻击分类和检测技术的分类.

结论:

  • 有效的DDoS攻击检测对于网络安全和CPS完整性至关重要.
  • 需要进一步的研究来优化基于ML的对高速网络的检测.
  • 在HSN中应对DDoS攻击的挑战需要创新的解决方案和持续的调查.