Comparative Analysis of SQL Injection Attack Clasification Using Naïve Bayes Method And Support Vector Machine (SVM).

Pramono, Pramono
Dwi Irawan, Ridwan
Arum Sari, Aprilisa

Proceeding of International Conference Health, Science And Technology (ICOHETECH)
2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)

SQL Injection is an attack that attempts to gain unauthorized access to a database by injecting code and exploiting SQL queries. SQL injection is an attack that is easy to execute but difficult to detect and classify because of the many types. The SQLI vulnerability is the result of incorrect validation of user input, enabling attackers to manipulate programmer queries by adding new SQL operators. Therefore, this study compares the use of the Naïve Bayes algorithm with the Support Vector Machine (SVM). The dataset that will be used in this study comes from a website called Kaggle. This study analyzes the comparison of methods resulting from the classification process based on the value of accuracy of confusion matrix, precision, recall. Naive Bayes, 95.594% accuracy quality while Support Vector Machine (SVM) 96.093% accuracy quality. The highest percentage of accuracy is obtained by the Support Vector Machine (SVM) while the Naïve Bayes accuracy score is slightly lower.