Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

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

Federated ConvNeXt-swin temporal fusion network for malware and botnet detection in IoT systems.

Scientific reports·2026

Same author

Lightweight malicious URL detection using deep learning and large language models.

Scientific reports·2025

Same author

Enhancing indoor monitoring of visually impaired people using temporal convolutional network with optimization model in IoT environment.

Scientific reports·2025

Same author

Adaptive malware identification via integrated SimCLR and GRU networks.

Scientific reports·2025

Same author

Electric vehicles charging station allocation based on load profile forecasting and Dijkstra's algorithm for optimal path planning.

Scientific reports·2025

Same author

Smart indoor monitoring for disabled individuals using an ensemble of deep learning models in an IoT environment.

Scientific reports·2025

Search research articles

相关实验视频

Updated: Sep 17, 2025

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

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

Published on: October 27, 2023

智能深度学习模型用于增强的物联网入侵检测和检测.

Faisal S Alsubaei¹

¹Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia. fsalsubaei@uj.edu.sa.

Scientific reports

|July 2, 2025

概括

此摘要是机器生成的。

这项研究通过优化机器学习模型来检测网络入侵来增强网络安全. 优化的XGBoost和OSNN模型在识别跨多个数据集的复杂网络威胁方面表现出高准确性.

关键词:

网络安全网络安全网络安全物联网 (IoT) 的物联网 (IoT) 的物联网.侵入检测入侵检测系统侵入者检测系统 (IDS) 是指入侵者检测系统.优化了XGBoost的优化优化的顺序神经网络.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

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

Published on: December 15, 2023

相关实验视频

Last Updated: Sep 17, 2025

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

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

Published on: October 27, 2023

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

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

Published on: December 15, 2023

科学领域:

网络安全网络安全
机器学习机器学习
网络入侵检测网络入侵检测

背景情况:

物联网 (IoT) 产生了大量的数据,需要强大的网络安全来打击复杂的网络攻击.
基于机器学习的异常检测提供了一个有希望的方法来识别异常的网络流量,表明入侵.
目前的方法因传统模型的预处理和超参数调整的局限性而难以应对不断变化的威胁.

研究的目的:

通过优化机器学习和深度学习模型来解决现有的入侵检测系统的局限性.
提高跨不同数据集的多类入侵检测准确性和效率.
提高在物联网环境中复杂,不断变化的网络威胁的检测.

主要方法:

实施了广泛的预处理步骤,然后对XGBoost和序列神经网络 (OSNN) 算法进行网格搜索超参数调整.
使用各种过器,内核,激活函数和规范化技术增强深度学习模型.
在NSL-KDD,UNSW-NB15和CICIDS2017数据集上全面测试了拟议的系统.

主要成果:

优化的XGBoost在NSL-KDD上实现了99.93%的准确性,高F1分数和MCC,低假阳性率 (FPR).
优化的SNN在NSL-KDD上显示了99.0%的准确性和1.00的AUC.
在UNSW-NB15上,OSNN模型达到96.80%的准确性,在CICIDS2017上达到99.53%的准确性,损失值较低.

结论:

优化OSNN模型的卓越性能归因于仔细的超参数调整,包括激活功能,学习速度和规范化.
拟议的方法显示了提高入侵检测,系统完整性和欺诈预防的巨大潜力.
这种方法为面对先进的网络威胁优化整体网络性能提供了一条途径.