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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.0K
What Are Outliers?01:12

What Are Outliers?

3.8K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
3.8K
Outliers and Influential Points01:08

Outliers and Influential Points

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
Unusual Results01:16

Unusual Results

3.2K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
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相关实验视频

Updated: Jun 21, 2025

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

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对于按键键的异常值检测生物识别用户身份验证.

Mahmoud G Ismail1, Mohammed A-M Salem1, Mohamed A Abd El Ghany2,3

  • 1Faculty of Media Engineering and Technology, German University in Cairo, Cairo, Egypt.

PeerJ. Computer science
|July 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用键式动态验证用户身份验证的新方法,消除了对伪造者数据的需求. 基于直方图的异常值得分 (HBOS) 为网络安全提供了更实用和更准确的方法.

关键词:
卡内基梅隆大学 (CMU) 的键盘键入生物识别数据集.基于历史图的异常值得分.键盘键入生物识别技术机器学习是机器学习.异常值检测异常值的检测用户身份验证用户身份验证

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An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones
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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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相关实验视频

Last Updated: Jun 21, 2025

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

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An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones
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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 生物识别信息 生物识别信息

背景情况:

  • 用户身份验证对于信息安全至关重要,面临身份欺诈和数据泄露的挑战.
  • 按键动态研究传统上依赖于伪造者数据集,在现实世界中很难获得这些数据集.

研究的目的:

  • 介绍一种新的方法,用于使用无监督异常值检测的按键动态认证.
  • 消除在认证系统中需要伪造者样本的必要性.

主要方法:

  • 使用无监督异常值检测技术,特别是基于直方图的异常值得分 (HBOS).
  • 将HBOS与15种其他异常检测方法进行比较.
  • 验证了卡内基梅隆大学 (CMU) 键式生物识别数据集的方法.

主要成果:

  • 与其他15种异常值检测方法相比,HBOS表现优越.
  • 实现了5.97%的相同错误率 (EER).
  • 获得的ROC曲线下的面积 (AUC) 为97.79%,准确度 (ACC) 为89.23%.

结论:

  • 拟议的HBOS方法通过消除对假冒者数据的需求,在按键动态身份验证方面取得了重大进展.
  • 这种方法提高了实际应用性,并解决了模拟欺诈键盘按的现实挑战.
  • 该方法提供了一个可靠和高效的解决方案,提高了用户身份验证的准确性和稳定性.