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相关概念视频

Chi-square Analysis02:46

Chi-square Analysis

43.4K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
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Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

4.2K
Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
4.2K
Chi-square Distribution01:10

Chi-square Distribution

6.3K
How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has...
6.3K
Detection of Black Holes01:10

Detection of Black Holes

2.5K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.5K
Test for Homogeneity01:23

Test for Homogeneity

2.4K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.4K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.8K
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...
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相关实验视频

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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使用千平方特征选择和机器学习分类器进行增强的SQL注入检测.

Emanuel Casmiry1, Neema Mduma1, Ramadhani Sinde1

  • 1Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania.

Frontiers in big data
|December 5, 2025
PubMed
概括

这项研究增强了使用Chi-square特征选择和机器学习的结构化查询语言 (SQL) 注入检测. 功能选择显著提高了分类器的性能,决策树实现了99.73%的准确性,用于强大的网络攻击预防.

科学领域:

  • 网络安全和机器学习
  • 数据科学和网络安全数据科学和网络安全

背景情况:

  • 结构化查询语言 (SQL) 注入攻击是一种普遍且昂贵的网络威胁,占全球网络攻击费用的20%以上.
  • 由于这些攻击的动态性质,现有的SQL注入检测方法与高假阳性率和低于最佳准确性作斗争.

研究的目的:

  • 通过将Chi-square特征选择与机器学习模型集成,提出一种用于检测SQL注入漏洞的增强方法.
  • 评估特征选择对各种分类算法的性能影响,以提高SQL注入检测准确度.

主要方法:

  • 通过将定制数据与SQLiV3.csv数据集合并,创建了一个混合数据集.
  • 术语频率-反向文档频率 (TF-IDF) 用于将SQL查询转换为数值特征向量.
  • 应用了千平方特征选择来识别和保留最相关的特征,随后测试了五个机器学习分类器 (多项纳夫贝叶斯,SVM,后勤回归,决策树,KNN).

主要成果:

  • 千平方特征选择通过减轻噪音和删除多余特征,在所有测试模型中明显提高了分类性能.
  • 决策树和K-最近邻居 (KNN) 模型在特征选择后表现出显著的性能增长.
  • 决策树分类器实现了最高的准确性 (99.73%),精度 (99.72%),回忆 (99.70%),和F1得分 (99.71%),具有较低的假阳性率 (0.25%) 和错误分类率 (0.27%).
关键词:
这是SQL注入的SQL注入.网络攻击 网络攻击功能选择 功能选择高维数据的高维数据.机器学习是机器学习.

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结论:

  • 特性选择对于提高准确性和降低高维数据环境中的假阳性率至关重要,用于SQL注入检测.
  • 该研究确立了特征选择是开发可靠和有效的SQL注入检测系统的重要组成部分,特别是用于改进集合和基于树的模型.