Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

128
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
128

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Deep Learning-Assisted Three-Dimensional Segmentation of Vertebrobasilar Artery Calcification in Cone Beam Computed Tomography.

Journal of imaging informatics in medicine·2026
Same author

Hybrid multi-scale CNN-Residual-LSTM approach for robust state-of-charge estimation in lithium-ion batteries.

Scientific reports·2026
Same author

Grad-CAM Enhanced Explainable Deep Learning for Multi-Class Lung Cancer Classification Using DE-SAMNet Model.

Diagnostics (Basel, Switzerland)·2026
Same author

Deep Learning-Assisted Detection and Classification of Thymoma Tumors in CT Scans.

Diagnostics (Basel, Switzerland)·2025
Same author

A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI Approach.

Journal of imaging informatics in medicine·2025
Same author

Classification of Brain Tumors in MRI Images with Brain-CNXSAMNet: Integrating Hybrid ConvNeXt and Spatial Attention Module Networks.

Interdisciplinary sciences, computational life sciences·2025

相关实验视频

Updated: Jul 3, 2025

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

539

使用卷积神经网络和基于决策树的分类来检测基于SDN的SCADA系统中的DDoS流行攻击的多阶段学习框架.

Onur Polat1, Muammer Türkoğlu2, Hüseyin Polat3

  • 1Department of Computer Engineering, Bingöl University, Bingöl 12000, Turkey.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种多阶段的学习模型,用于检测基于软件定义网络 (SDN) 的监督控制和数据采集 (SCADA) 系统中的分布式拒绝服务 (DDoS) 攻击,达到97.8%的准确性.

关键词:
在美国,CNN是CNN.对于DDoS攻击来说,这是一次性攻击.这是一个 SCADA 系统.在SDN中,SDN是SDN.关键基础设施 关键基础设施网络大流行 网络大流行机器学习是机器学习.

更多相关视频

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

755
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

相关实验视频

Last Updated: Jul 3, 2025

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

539
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

755
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 工业控制系统 工业控制系统

背景情况:

  • 由于传统的网络结构,传统的监督控制和数据采集 (SCADA) 系统面临着灵活性,可扩展性和管理方面的挑战.
  • 软件定义网络 (SDN) 通过分离控制和数据层提供了潜在的解决方案,但引入了新的漏洞,特别是针对针对其集中控制器的分布式拒绝服务 (DDoS) 攻击.
  • 有效地检测DDoS攻击对于防止基于SDN的SCADA环境中严重中断至关重要.

研究的目的:

  • 提出和评估一种新的多阶段学习模型,用于有效检测基于SDN的SCADA系统中的DDoS攻击.
  • 解决通过将SDN集成到SCADA网络中引入的安全漏洞.
  • 提高关键工业基础设施对复杂的网络威胁的弹性.

主要方法:

  • 开发一个多阶段的学习模型,结合一维卷积神经网络 (1D-CNN) 和基于决策树的分类.
  • 为培训和测试,创建一个新的数据集,在特定的实验网络拓中提供各种攻击场景,用于培训和测试.
  • 实验验证拟议模型在检测DDoS攻击方面的性能.

主要成果:

  • 拟议的多阶段学习模型在检测DDoS攻击方面实现了97.8%的高准确率.
  • 该模型证明了在基于SDN的SCADA网络中有效识别各种攻击场景.
  • 突出了早期检测能力,使得安全措施能够及时实施.

结论:

  • 开发的多阶段学习模型对于检测基于SDN的SCADA系统中的DDoS攻击非常有效.
  • 这种方法在保护关键工业控制基础设施方面取得了重大进展.
  • 这些发现强调了先进的机器学习技术在工业环境中提供强大的网络安全的潜力.