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Detection of Structured Query Language Injection Attacks Using Machine Learning Techniques


Affiliations
1 Department of Computing, Mathematical and Statistical Sciences, University of Namibia, Windhoek, Namibia
 

This paper presents a comparative analysis of various machine learning classification models for structured query language injection prevention. The objective is to identify the best-performing model in terms of accuracy on a given dataset. The study utilizes popular classifiers such as Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Based on the tests used to evaluate the performance of the classifiers, the Naïve Bayes gets the highest level of accurate detection. The results show a 97.06% detection rate for the Naïve Bayes, followed by LogisticRegression (0.9610), Support Vector Machine (0.9586), RandomForest (0.9530), DecisionTree (0.9069), and K-Nearest Neighbor (0.6937). The code snippet provided demonstrates the implementation and evaluation of these models.

Keywords

Classification models, SQL-I, Python, Machine learning, Evaluations
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  • Detection of Structured Query Language Injection Attacks Using Machine Learning Techniques

Abstract Views: 54  |  PDF Views: 34

Authors

Taapopi John Angula
Department of Computing, Mathematical and Statistical Sciences, University of Namibia, Windhoek, Namibia
Valerianus Hashiyana
Department of Computing, Mathematical and Statistical Sciences, University of Namibia, Windhoek, Namibia

Abstract


This paper presents a comparative analysis of various machine learning classification models for structured query language injection prevention. The objective is to identify the best-performing model in terms of accuracy on a given dataset. The study utilizes popular classifiers such as Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Based on the tests used to evaluate the performance of the classifiers, the Naïve Bayes gets the highest level of accurate detection. The results show a 97.06% detection rate for the Naïve Bayes, followed by LogisticRegression (0.9610), Support Vector Machine (0.9586), RandomForest (0.9530), DecisionTree (0.9069), and K-Nearest Neighbor (0.6937). The code snippet provided demonstrates the implementation and evaluation of these models.

Keywords


Classification models, SQL-I, Python, Machine learning, Evaluations

References