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

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

Strategic Planning for a Digital Health Innovation Hub at a Saudi Academic Medical Center: Qualitative Case Study.

JMIR formative research·2026
Same author

Epidemiology, Molecular Characteristics, Treatment Patterns, and Outcomes of Carbapenem-Resistant Enterobacterales Infections in a Tertiary Hospital in Southern Saudi Arabia: A Retrospective Cohort Study.

Journal of epidemiology and global health·2026
Same author

The relationship of apical periodontitis with chronic diseases and smoking habits: an umbrella review.

Odontology·2026
Same author

Sodium-Glucose Cotransporter 2 Inhibitors as Discharge Medications in Survivors of Acute Myocardial Infarction Complicated by Cardiogenic Shock.

Shock (Augusta, Ga.)·2026
Same author

Epidemiology, antimicrobial resistance, and mortality of bloodstream infections in hemodialysis patients: an 11-year retrospective cohort study from southern Saudi Arabia.

BMC nephrology·2026
Same author

Cytotoxic Effects of the Synthetic Cannabinoid, 5F-MDMB-PICA on Human Glioblastoma U87-MG Cells.

International journal of medical sciences·2026

相关实验视频

Updated: Jun 21, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

机器学习算法的比较评价用于网络鱼网站检测和检测.

Noura Fahad Almujahid1, Mohd Anul Haq2, Mohammed Alshehri1

  • 1Department of Information Technology, College of Computer and Information Science, Majmaah University, Majmaah, Riyadh, Saudi Arabia.

PeerJ. Computer science
|July 10, 2024
PubMed
概括

本研究评估了用于检测网络鱼网站的机器学习 (ML) 和深度学习 (DL) 算法. 卷积神经网络 (CNN) 模型在识别恶意URL方面表现出卓越的准确性,提供了针对在线盗窃的增强保护.

关键词:
分类 分类 分类 分类.机器学习是机器学习.网络鱼 (phishing) 是一种欺诈行为.网络鱼检测 网络鱼检测

更多相关视频

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K
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

相关实验视频

Last Updated: Jun 21, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K
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

科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 互联网技术的兴起导致了电子交易的增加和针对敏感用户信息的网络鱼攻击的激增.
  • 网络鱼构成重大威胁,其目的是为了经济利益或身份盗窃而窃取机密数据.
  • 现有的反鱼方法需要加强,以有效地识别鱼URL.

研究的目的:

  • 评估八个机器学习 (ML) 和深度学习 (DL) 算法的识别网络鱼URL的性能.
  • 为了比较包括SVM,KNN,RF,DT,XGBoost,LR和CNN在内的算法的有效性.
  • 为了确定最有效的DL/ML模型来实现强大的反鱼技术.

主要方法:

  • 利用两个现实世界数据集 (门德利和UCI) 进行模型培训和评估.
  • 采用了性能指标,如准确性,精度,回忆,假阳性率 (FPR) 和F1评分.
  • 实现了功能工程,针对类不平衡的SMOTE,以及所有模型的严格超参数调整,包括一种新的CNN方法.

主要成果:

  • 与其他评估的算法相比,卷积神经网络 (CNN) 模型在网络鱼URL检测方面取得了更高的准确性.
  • 所有测试的ML和DL模型在两个数据集中都显示出一致的性能,表明稳定性.
  • 这项研究强调了CNN在打击网络鱼威胁方面的有效性.

结论:

  • 机器学习和深度学习,特别是CNN,为先进的反鱼技术提供了有前途的解决方案.
  • 开发的模型在识别网络鱼URL方面提供了可靠和稳定的性能.
  • 进一步研究和实施基于CNN的系统可以显著提高用户对在线欺诈的安全性.