Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model
SQL injection attacks are one of the most common types of attacks on Web applications. These attacks exploit vulnerabilities in an application’s database access mechanisms, allowing attackers to execute unauthorized SQL queries. In this study, we propose an architecture for detecting SQL injection a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/15/3286 |
| _version_ | 1797586376779104256 |
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| author | Maha Alghawazi Daniyal Alghazzawi Suaad Alarifi |
| author_facet | Maha Alghawazi Daniyal Alghazzawi Suaad Alarifi |
| author_sort | Maha Alghawazi |
| collection | DOAJ |
| description | SQL injection attacks are one of the most common types of attacks on Web applications. These attacks exploit vulnerabilities in an 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 autoencoder. The proposed architecture was trained on a publicly available dataset of SQL injection attacks. Then, it was compared with several other machine learning models, including ANN, CNN, decision tree, naive Bayes, SVM, random forest, and logistic regression models. The experimental results 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 to the other models covered in the study. |
| first_indexed | 2024-03-11T00:22:24Z |
| format | Article |
| id | doaj.art-5bd13e6c87ac444fa286aa41551dbc7b |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:22:24Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-5bd13e6c87ac444fa286aa41551dbc7b2023-11-18T23:14:36ZengMDPI AGMathematics2227-73902023-07-011115328610.3390/math11153286Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder ModelMaha Alghawazi0Daniyal Alghazzawi1Suaad Alarifi2Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi ArabiaInformation Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi ArabiaInformation Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi ArabiaSQL injection attacks are one of the most common types of attacks on Web applications. These attacks exploit vulnerabilities in an 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 autoencoder. The proposed architecture was trained on a publicly available dataset of SQL injection attacks. Then, it was compared with several other machine learning models, including ANN, CNN, decision tree, naive Bayes, SVM, random forest, and logistic regression models. The experimental results 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 to the other models covered in the study.https://www.mdpi.com/2227-7390/11/15/3286SQL injection attacksrecurrent neural network (RNN) autoencoderANNCNNdecision treenaive Bayes |
| spellingShingle | Maha Alghawazi Daniyal Alghazzawi Suaad Alarifi Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model Mathematics SQL injection attacks recurrent neural network (RNN) autoencoder ANN CNN decision tree naive Bayes |
| title | Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model |
| title_full | Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model |
| title_fullStr | Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model |
| title_full_unstemmed | Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model |
| title_short | Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model |
| title_sort | deep learning architecture for detecting sql injection attacks based on rnn autoencoder model |
| topic | SQL injection attacks recurrent neural network (RNN) autoencoder ANN CNN decision tree naive Bayes |
| url | https://www.mdpi.com/2227-7390/11/15/3286 |
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