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A Hybrid Deep Learning Model for SQL Injection Attack Detection
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DOI：10.1109/ACCESS.2026.3651991.png)
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Abstract
En 中文
An increasing number of web application services raises significant security concerns. Online access to these applications exposes them to multiple cyberattacks. The Open Web Application Security Project has consistently reported SQL injection attacks among the top 10 cyber threats for over a decade, highlighting the need for more effective detection and prevention strategies. Traditional detection methods, primarily signature-based, offer basic protection but struggle to identify new signatures embedded within web requests. An alternative involves Machine Learning (ML) and Deep Learning predictive analytics, which provide effective solutions for analyzing large datasets to detect and prevent SQL Injection Attacks (SQLIA). However, these models often face challenges such as high false alarm rates and low accuracy. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model for SQL injection detection. Five architectures were compared: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), hybrid CNN-LSTM, and hybrid CNN-GRU. Among them, CNN-LSTM achieved the best results, with 99.85% accuracy, 99.86% precision, 99.85% recall, and a 99.85% F1-score, while also recording the lowest false positive rate (0.06%) and misclassification rate (0.15%). CNN-GRU, CNN, and GRU followed closely, though GRU reported the highest false positive rate (0.16%). The standalone LSTM model performed slightly weaker, with 99.80% accuracy, 99.80% precision, 99.79% recall, and 99.80% F1-score, along with false positive and misclassification rates of 0.06% and 0.20%, respectively. Overall, the findings show that all models achieved near-perfect performance, with hybrid architectures outperforming single-architecture models.
Keywords:
Deep learning
natural language processing
SQL injection (SQLi)
cyberattack
hybrid architectures
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