Improving Detection of SQL Injection Attack by SMOTE

This paper proposes competitive efficiency study and methods for detecting SQL injection by using rule base and machine learning. Rule-base employs the black-list word technique while machine learning includes Naive Bayes, random forest, and the K-Nearest Neighbor. We also apply SMOTE to improve the prediction rate on a minority class (injection class). As a result, machine learning algorithms that use the random forest and KNN efficiently detect very similar patterns as good as rule-based detection.