Deep learning approaches to SQL injection detection: evaluating ANNs, CNNs, and RNNs

Majid Alshammari

doi:10.1117/12.3012620

19 December 2023 Deep learning approaches to SQL injection detection: evaluating ANNs, CNNs, and RNNs

Majid Alshammari

Proceedings Volume 12936, International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023); 129360H (2023) https://doi.org/10.1117/12.3012620
Event: International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023), 2023, Istanbul, Turkey

Abstract

In the digital era, SQL injection (SQLi) attacks on web applications pose significant threats to data integrity and security. While traditional methods such as signature-based and anomaly-based detections have some limitations, this research explores the application of neural networks in countering these attacks. Specifically, this research evaluates the performance of three primary neural network architectures: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for SQLi attack detection. The research methodology involves converting text-based SQL queries into numeric values suitable and compatible with the neural networks, using Term Frequency-Inverse Document Frequency (TF-IDF), tokenization, and padding. Results show that the CNN outperforms in almost all metrics, with RNNs following closely and ANNs achieving the lower results.

Citation Download Citation

Majid Alshammari "Deep learning approaches to SQL injection detection: evaluating ANNs, CNNs, and RNNs", Proc. SPIE 12936, International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023), 129360H (19 December 2023); https://doi.org/10.1117/12.3012620

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available