Stealthy SQL injections using obfuscation, alternative encodings, and semantically veiled constructs remain a hard-to-detect threat to web applications. The paper proposes the INSECT framework, which combines formal grammatical analysis of SQL queries with semantic modeling of intentions based on LLMs. The first layer detects structural anomalies, while the second recognizes malicious intent in syntactically correct queries. Experiments on a dataset of 4,800 queries, including 1,200 obfuscated injections, demonstrated 96.7% accuracy with a 2.1% false positive rate, outperforming signature-based, grammatical, and neural network baseline approaches.
| Mualliflar | Иргашева , Дурдона, Гаипназаров , Рустам, Мухтарова , Гулнора |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-04-28 |
| Son | 2 |
| Betlar | 55-63 |
| Til | Rus |
SQL injection, large language models, semantic intent analysis, formal grammar, obfuscation, cybersecurity., SQL-инъекции, большие языковые модели, семантический анализ намерений, формальная грамматика, обфускация, кибербезопасность., SQL-inʼeksiya, katta til modellari, semantik niyat tahlili, formal grammatika, obfuskatsiya, kiberxavfsizlik
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