Alghawazi, M.; Alghazzawi, D.; Alarifi, S. Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model. Mathematics2023, 11, 3286.
Alghawazi, M.; Alghazzawi, D.; Alarifi, S. Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model. Mathematics 2023, 11, 3286.
Alghawazi, M.; Alghazzawi, D.; Alarifi, S. Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model. Mathematics2023, 11, 3286.
Alghawazi, M.; Alghazzawi, D.; Alarifi, S. Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model. Mathematics 2023, 11, 3286.
Abstract
SQL injection attacks are one of the most common types of attacks on web applications. These attacks exploit vulnerabilities in the application’s database access mechanisms, allowing attackers to execute unauthorized SQL queries. In this study, we propose an architecture for detecting SQL injection attacks using a recurrent neural network (RNN) autoencoder. The proposed architecture was trained on a publicly available dataset of SQL injection attacks. Then compared with several other machine learning models, including ANN, CNN, Decision Tree, Naïve Bayes, SVM, Random Forest, and Logistic Regression. The experimental result showed that the proposed approach achieved an accuracy of 94% and an F1 score of 92%, which demonstrate its effectiveness in detecting QL injection attacks with high accuracy in comparison with other models covered in the study.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
