2026 5th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE)
2026ConferenceCyberBiLSTM: A Bidirectional LSTM Architecture for Cybersecurity via High-Precision SQL Injection Attack Detection
This work introduces CyberBiLSTM, a deep learning model trained on a newly aggregated and diverse dataset of over 244,112 SQL injection queries. The model achieved a test accuracy of 98.43% and a precision of 99.79%, outperforming five benchmark classifiers. In particular, these results were obtained in just 15 epochs, demonstrating rapid convergence and strong generalization. The dataset’s diversity—sourced from seven public repositories and one proprietary corpus—enabled the model to learn complex adversarial patterns beyond simple token-level features. Comparative evaluations and ROC/confusion matrix analyses confirm CyberBiLSTM’s superiority in both precision and robustness. This foundation sets the stage for real-world deployment and future extensions, including explainability, zero-day attack detection, and integration into developer-facing security tools.
