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

Survival Tree01:19

Survival Tree

66
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
66

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

Data-driven prediction and thermodynamic performance assessment of industrial cooling towers using advanced machine learning algorithms.

PloS one·2026
Same author

Prevalence of dental caries among schoolchildren in Saudi Arabia.

Eastern Mediterranean health journal = La revue de sante de la Mediterranee orientale = al-Majallah al-sihhiyah li-sharq al-mutawassit·2026
Same author

Evaluation and Optimization of Azithromycin Removal by Raw and Alkali-Modified Peanut Shells Using Taguchi-Based Experimental Design Approach.

Water environment research : a research publication of the Water Environment Federation·2026
Same author

Barriers and enablers affecting female participation in physical activity.

Frontiers in global women's health·2026
Same author

Association Between Total Quality Management and Financial Performance: Evidence From University Teaching Hospitals.

Inquiry : a journal of medical care organization, provision and financing·2025
Same author

Challenges and opportunities of complexity theory in health care systems.

Journal of Taibah University Medical Sciences·2025

相关实验视频

Updated: Jun 12, 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

485

一个高效的入侵检测系统用于物联网安全使用CNN决策森林.

Kamal Bella1, Azidine Guezzaz1, Said Benkirane1

  • 1Technology Higher School Essaouira, Cadi Ayyad University, Essaouira, Morocco.

PeerJ. Computer science
|September 24, 2024
PubMed
概括

一个新的基于森林的深度神经决策入侵检测系统 (IDS) 增强了物联网 (IoT) 的安全性. 该系统实现了高精度和快速预测时间,通过高效地利用最小特征进行异常检测,优于现有模型.

关键词:
深度学习是一种深度学习.侵入检测入侵检测系统可以检测入侵.这就是为什么物联网物联网物联网.机器学习是机器学习.安全的安全的安全的安全的安全.

更多相关视频

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K
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 12, 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

485
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K
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

科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 物联网 (IoT) 安全 安全 物联网

背景情况:

  • 物联网设备的广泛采用增加了对网络攻击的脆弱性.
  • 侵入检测系统 (IDS) 对于保护物联网生态系统至关重要.
  • 现有的IDS方法需要改进,以提高准确性和效率.

研究的目的:

  • 引入一种新的基于深度神经决策森林的IDS (DNDF-IDS),用于增强网络异常检测.
  • 提高物联网环境中入侵检测的准确性和效率.
  • 通过使用各种特征选择技术来评估DNDF-IDS的性能.

主要方法:

  • 开发了一个新的DNDF-IDS,将决策森林与神经网络集成在一起.
  • 应用了四种特征选择方法:PCA,LASSO,SelectKBest和RFFI.
  • 在三个基准数据集上对模型进行了评估:NSL-KDD,CICIDS2017和UNSW-NB15.

主要成果:

  • 在数据集中实现了高精度 (ACC),从94.09%到98.84%不等.
  • 证明每条记录的预测时间为0.1毫秒.
  • 超过或匹配最近的随机森林和CNN基于的模型,特别是十大特征.

结论:

  • DNDF-IDS为物联网安全提供了一个高度准确和高效的解决方案.
  • 该模型有效地使用最小的功能集识别网络异常.
  • 这种方法显著提高了入侵检测中的计算资源利用率.