Jove
Visualize
联系我们

相关实验视频

EM-AUC:一种用于评估基于异常的网络入侵检测系统的新算法.

Kevin Z Bai1, John M Fossaceca2

  • 1Independent Researcher, Westwood, MA 02090, USA.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
概括
此摘要是机器生成的。

相关概念视频

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

74
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
74
McNemar's Test01:23

McNemar's Test

141
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
141

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 journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章
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

这项研究引入了一种新的算法,用于评估网络入侵检测模型,而不需要标记数据. 预期最大化-曲线下面面积 (EM-AUC) 方法使得可靠的性能指标计算成为可能,改善了网络安全的模型选择.

科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 有效的网络入侵检测依赖于无监督的机器学习模型,但评估它们的性能通常需要标记数据.
  • 现实世界的网络数据集庞大,通常缺乏标签,使得传统的性能指标计算不可行.
  • 有需要的算法可以评估模型性能,而不依赖于地面真相标签.

研究的目的:

  • 提出一个新的算法,预期最大化-曲线下的面积 (EM-AUC),用于导出没有标签的性能指标.
  • 为了使在标签稀缺的环境中计算ROC曲线下的面积 (AUC-ROC) 和精度回忆曲线下的面积 (AUC-PR).
  • 为网络入侵检测系统提供具有成本效益和可扩展性的模型选择.

主要方法:

  • 开发了预期最大化-曲线下的面积 (EM-AUC) 算法.
  • 将不可用的标签视为缺少的数据,并使用后期概率计算它们.
  • 应用EM-AUC算法对两个网络入侵数据集进行评估.

主要成果:

  • 在不需要标记数据的情况下,成功导出了AUC-ROC和AUC-PR指标.
  • 证明了EM-AUC算法在网络入侵数据集上的强大性能评估能力.
  • 在没有标签的情况下,实现了无监督模型的性能指标计算.
关键词:
精确召回曲线下的面积罗克曲线下面的区域在EM-AUC算法中,使用EM-AUC算法.缺失的数据推理推理网络入侵检测检测 网络入侵检测无监督的机器学习模型

相关实验视频

结论:

  • 在没有标签的情况下,EM-AUC算法为评估网络入侵检测系统提供了一个新的解决方案.
  • 这种方法允许在没有全面标签的情况下进行模型培训,测试和性能评估,从而提高了可扩展性和成本效益.
  • 这代表了评估网络安全应用程序无监督异常检测模型的重大进步.