STUDIES IN ENGINEERING AND EXACT SCIENCES, Год журнала: 2024, Номер 5(2), С. e11299 - e11299

Опубликована: Ноя. 29, 2024

SQL injection attacks are a major danger to data security in application systems, leveraging weaknesses illicitly access and change sensitive data. Traditional detection methods, such rule-based systems supervised machine learning, struggle adapt new attack strategies. This study presents an Enhanced Deep Learning Framework for Injection Detection utilizing the Gated Recurrent Unit (GRU) model overcome constraints. To discover patterns, proposed framework uses dynamic learning process instead of static methods. By examining query sequences, can distinguish between legal malicious interactions without predefined rules or reinforcement learning. The framework's performance is assessed using broad dataset valid queries. Experiments show considerable increase accuracy, reaching 96.65% with little false positives. system resilient adaptable address complexity modern threats. results demonstrate effectiveness deep particularly GRU model, detecting attacks. research enhances database lays groundwork future cyber-security methods web-based applications.

Язык: Английский