Enhancing SQL Injection Prevention: Advanced Machine Learning and LSTM-Based Techniques

A kind of cybercrime known as SQL injection lets attackers alter records by running bogus SQL queries into an input field. This could result from more serious security breaches, illegal access to sensitive data, and data corruption. Using Deep Learning and Machine Learning techniques can help to reduce the major threat, SQL Injection attacks on web systems provide. With the aim of reducing SQL Injection, we investigated the construction and evaluation of various distinct Machine Learning and Deep Learning models. Our work aimed to investigate, in comparison to advanced Deep Learning models, especially Long Short-Term Memory networks, the performance of standard Machine Learning models. We conducted thorough tests to assess every model's per-formance in identifying attempts at SQL Injection. The results show that com-pared to conventional Machine Learning models, Deep Learning models, mostly Long Short-Term Memory networks, have outstanding performance. Their rates of false positives are reduced, and they get more accuracy. The results show the strong resilience of Long Short-Term Memory networks as a suitable strategy to improve online application security against SQL Injection risks.