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Chi-square Analysis02:46

Chi-square Analysis

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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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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...
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Chi-square Distribution01:10

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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...
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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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Not until the 1960s, when the first neutron...
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Test for Homogeneity01:23

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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...
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Detection of Gross Error: The Q Test01:00

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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...
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Enhanced SQL injection detection using chi-square feature selection and machine learning classifiers.

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
Summary

This study enhances Structured Query Language (SQL) injection detection using Chi-square feature selection and machine learning. Feature selection significantly boosted classifier performance, with Decision Tree achieving 99.73% accuracy for robust cyberattack prevention.

Keywords:
SQL injectioncyberattacksfeature selectionhigh-dimensional datamachine learning

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Area of Science:

  • Cybersecurity and Machine Learning
  • Data Science and Network Security

Background:

  • Structured Query Language (SQL) injection attacks are a prevalent and costly web threat, responsible for over 20% of global cyberattack expenses.
  • Existing SQL injection detection methods struggle with high false positive rates and suboptimal accuracy due to the dynamic nature of these attacks.

Purpose of the Study:

  • To propose an enhanced method for detecting SQL injection vulnerabilities by integrating Chi-square feature selection with machine learning models.
  • To evaluate the impact of feature selection on the performance of various classification algorithms for improved SQL injection detection accuracy.

Main Methods:

  • A hybrid dataset was created by merging custom data with the SQLiV3.csv dataset.
  • Term Frequency-Inverse Document Frequency (TF-IDF) was employed for converting SQL queries into numerical feature vectors.
  • Chi-square feature selection was applied to identify and retain the most relevant features, followed by testing five machine learning classifiers (multinomial Naïve Bayes, SVM, logistic regression, decision tree, KNN).

Main Results:

  • Chi-square feature selection demonstrably improved classification performance across all tested models by mitigating noise and removing redundant features.
  • The Decision Tree and K-Nearest Neighbors (KNN) models exhibited significant performance gains post-feature selection.
  • The Decision Tree classifier achieved the highest accuracy (99.73%), precision (99.72%), recall (99.70%), and F1-score (99.71%), with a low false positive rate (0.25%) and misclassification rate (0.27%).

Conclusions:

  • Feature selection is critical for enhancing the accuracy and reducing the false positive rates in high-dimensional data environments for SQL injection detection.
  • The study establishes feature selection as a vital component in developing reliable and effective SQL injection detection systems, particularly for improving ensemble and tree-based models.