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Detecting SQL Injection (SQLi) attacks is crucial for web-based data centersecurity, but it is challenging to balance accuracy and computationalefficiency, especially in high-speed networks. Traditional methods strugglewith this balance, while NLP-based approaches, although accurate, arecomputationally intensive.  We introduce a novel cascade SQLi detection method, blending classical andtransformer-based NLP models, achieving a 99.86% detection accuracy withsignificantly lower computational demands-20 times faster than usingtransformer-based models alone. Our approach is tested in a realistic settingand compared with 35 other methods, including Machine Learning-based andtransformer models like BERT, on a dataset of over 30,000 SQL sentences.  Our results show that this hybrid method effectively detects SQLi inhigh-traffic environments, offering efficient and accurate protection againstSQLi vulnerabilities with computational efficiency. The code is available athttps://github.com/gdrlab/cascaded-sqli-detection .

Advancing SQL Injection Detection for High-Speed Data Centers: A Novel Approach Using Cascaded NLP