Enhancing Web Application Firewalls with BERT-GNN for SQL Injection Detection
A hybrid BERT-GNN model for SQL injection detection aims to outperform traditional WAF signatures against obfuscated payloads, potentially raising the bar for web application protection.
Summary written by editorial AI · Source link below
arXiv:2608.28882v1 Announce Type: new Abstract: Detecting sophisticated SQL Injection (SQLi) attacks remains among the most critical challenges in web applications security. This research study has resulted in an optimised hybrid BERT-GNN pipeline with improved detection accuracy and robustness while reducing false-positive and false-negative rates. SQL queries are tokenised and encoded into contextual BERT embeddings, which then initialise the node features of a Graph Neural Network (GNN) trai
Editorial Analysis
Enterprises relying on signature-based WAFs face growing evasion risks; ML-augmented detection could close gaps against sophisticated SQLi variants.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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