Jove
Visualize
联系我们

相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

119
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:
119

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

Estimation of gut microbiome motif associated with active tuberculosis - A case control study.

Bioinformation·2026
Same author

ML-FSID-FIS: A Multi-Level Feature Selection and Fuzzy Inference System for Intrusion Detection in IoMT.

Sensors (Basel, Switzerland)·2026
Same author

Generation of biologically responsive colon-like intestinal tissue patches from human induced pluripotent stem cells using a rapid co-differentiation platform.

Stem cell research & therapy·2026
Same author

FedSMOTE-DP: Privacy-Aware Federated Ensemble Learning for Intrusion Detection in IoMT Networks.

Sensors (Basel, Switzerland)·2026
Same author

A Prospective Analysis of Maternal and Fetal Outcomes in Term Pregnancies Complicated by Oligohydramnios at Bundelkhand Medical College.

Journal of pharmacy & bioallied sciences·2026
Same author

Enhancing Tuberculosis Diagnosis and Treatment Monitoring: Evaluating Ki67 and HLA-DR Biomarkers Via Point-of-Care Testing and Comparative Analysis of Flow Cytometry Versus ELISA in Whole Blood Samples.

Journal of pharmacy & bioallied sciences·2026
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

相关实验视频

Updated: Jun 18, 2025

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

1.4K

使用机器学习多类分类技术实时检测物联网攻击

Ahmed Alrefaei1, Mohammad Ilyas1

  • 1Electrical Engineering & Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
概括

本研究介绍了针对物联网 (IoT) 攻击的实时入侵检测系统 (IDS),使用多类分类实现高精度. 该系统提供高效的预测时间,以提高网络安全性.

科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 物联网 (IoT) 设备的普及导致了复杂的网络攻击的增加.
  • 现有的入侵检测系统 (IDS) 往往难以应对物联网网络流量的数量和速度.
  • 需要有效和准确的实时检测物联网特定威胁.

研究的目的:

  • 开发和评估实时入侵检测系统 (IDS),用于识别物联网 (IoT) 攻击.
  • 通过使用多类分类模型来提高检测准确度并最大限度地减少预测时间.
  • 为了利用PySpark架构在网络安全中进行可扩展的大数据处理.

主要方法:

  • 利用IoT-23数据集,包括智能家居物联网设备的网络流量.
  • 应用数据预处理技术,包括清理,转换,缩放和合成少数群体过量采样技术 (SMOTE).
  • 在PySpark框架内使用OneVsRest (OVR) 技术和特征选择方法的多类分类算法.

主要成果:

  • 极端梯度提升在检测物联网攻击方面实现了98.89%的高精度.
  • 随机森林展示了最快的预测时间在0.0311秒.
  • 拟议的IDS与现有实时检测方法相比,表现优越.
关键词:
物联网的物联网,就是物联网.这是一个PySpark的架构.侵入检测系统的入侵检测系统机器学习是机器学习.

更多相关视频

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

相关实验视频

Last Updated: Jun 18, 2025

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

1.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

结论:

  • 开发的实时IDS有效地检测物联网攻击,具有高准确性和效率.
  • PySpark 架构为入侵检测网络流量的可扩展和快速分析提供了便利.
  • 这项研究为确保物联网设备不断扩展的景观提供了强大的解决方案.