ENHANCING SQL INJECTION (SQLI) MITIGATION BY REMOVING MALICIOUS SQL PARAMETER VALUES USING LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORKS

Web applications are increasingly targeted by cyber attacks. With SQL injection being a significant vulnerability, it caused an estimated USD4 billion global economic loss in 2022. Research has been conducted to explore methods to mitigate the impact of these attacks either by detecting them immediately or preventing them in their tracks. However, conventional approaches such as rule-based or signature-based systems have limitations as they cannot adapt to new or obscure attack patterns. This study explores the potentials of using Long Short-Term Memory (LSTM) neural network-based architectures for sanitization of SQL parameter values from malicious characters and shows that the result demonstrates that LSTM is the top performer, consistently achieving near-perfect accuracy, precision, recall, and F1-scores of 99.66% effectively. In a nutshell, this work has made a very strong and scalable contribution to web application security problems and demonstrates the possibility of SQLi mitigation using LSTM networks. By addressing such a critical gap in the literature, this study is a significant progress in the field of cyber security and robustness against obfuscation of attacks.