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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

131
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:
131
Random Error01:04

Random Error

888
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
888
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.5K
Sampling Plans01:23

Sampling Plans

187
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
187
Systematic Sampling Method01:17

Systematic Sampling Method

10.4K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
10.4K
Random Sampling Method01:09

Random Sampling Method

11.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.2K

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

A Korean native halophyte extract attenuates the virulence of methicillin-resistant Staphylococcus aureus by inhibiting biofilm formation.

Scientific reports·2026
Same author

FP-ZOO: Fast Patch-Based Zeroth Order Optimization for Black-Box Adversarial Attacks on Vision Models.

Sensors (Basel, Switzerland)·2025
Same author

<i>Yarrowia lipolytica</i> as a promising cell factory for microbial production of value-added nutraceuticals.

Frontiers in bioengineering and biotechnology·2025
Same author

Regenerative Capacity of Alveolar Type 2 Cells Is Proportionally Reduced Following Disease Progression in Idiopathic Pulmonary Fibrosis-Derived Organoid Cultures.

Tuberculosis and respiratory diseases·2024
Same author

Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review.

Sensors (Basel, Switzerland)·2024
Same author

A Synthetic Time-Series Generation Using a Variational Recurrent Autoencoder with an Attention Mechanism in an Industrial Control System.

Sensors (Basel, Switzerland)·2024
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

相关实验视频

Updated: Jul 7, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

基于系统抽样和线性回归,生成反映网络攻击的ICS异常数据.

Ju Hyeon Lee1, Il Hwan Ji1, Seung Ho Jeon2

  • 1Department of Information Security, Gachon University, Seongnam-si 1342, Republic of Korea.

Sensors (Basel, Switzerland)
|December 23, 2023
PubMed
概括
此摘要是机器生成的。

为工业控制系统 (ICS) 生成现实的网络攻击数据现在更快,更具成本效益. 这种新方法为测试安全设备和培训创造了有价值的异常数据,克服了以前的局限性.

关键词:
产生异常数据的数据生成.网络攻击数据数据网络安全 网络安全工业控制系统 工业控制系统机器学习是机器学习.

更多相关视频

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

3.2K
A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
00:08

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.0K

相关实验视频

Last Updated: Jul 7, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

3.2K
A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
00:08

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.0K

科学领域:

  • 网络安全 网络安全
  • 工业控制系统 (ICS) 是指工业控制系统.
  • 数据生成 数据生成

背景情况:

  • 由于信息和通信技术 (ICT) 的整合,工业控制系统 (ICS) 面临越来越多的网络威胁.
  • 需要现实的异常数据来测试安全设备和有效培训人员.
  • 在ICS环境中获得足够的异常数据存在挑战.

研究的目的:

  • 提出一种用于生成异常数据的新方法,该方法可以准确地反映ICS中的网络攻击特征.
  • 为了克服当前异常数据采集方法的成本,时间和数据可用性的局限性.

主要方法:

  • 使用对良性ICS数据的系统采样和线性回归模型.
  • 采用统计分析来识别和改变表明网络攻击模式的特征.
  • 使用基于Modbus的ICS_PCAPS数据生成超过5万个新的异常数据点.

主要成果:

  • 生成的异常数据显示了从良性数据到攻击数据的模式转变,核密度估计证实了这一点.
  • 使用新生成的数据对现有模型进行训练,并没有显示出显著的性能下降.
  • 该方法成功创建了部分反映攻击数据特征的异常数据.

结论:

  • 拟议的方法提供了一种快速,逻辑和资源高效的方法,用于为ICS生成类似网络攻击的异常数据.
  • 这有助于改进安全措施的测试,并加强网络训练.
  • 解决了在ICS网络安全研究中对可访问和代表性的异常数据集的关键需求.