Effect of Deep Recurrent Architectures on Code Vulnerability Detection: Performance Evaluation for SQL Injection in Python

Security defects in software code can lead to situations that compromise web-based systems, data security, service availability, and the reliability of functionality. Therefore, it is crucial to detect code vulnerabilities as early as possible. During the research, the architectures of the deep learning models, peephole LSTM, GRU-Z, and GRU-LN, their element regularizations, and their hyperparameter settings were analysed to achieve the highest performance in detecting SQL injection vulnerabilities in Python code. The results of the research showed that after investigating the effect of hyperparameters on Word2Vector embeddings and applying the most efficient one, the peephole LSTM, delivered the highest performance (F1 = 0.90)—surpassing GRU-Z (0.88) and GRU-LN (0.878)—thereby confirming that the access of the peephole connections to the cell state produces the highest performance score in the architecture of the peephole LSTM model. Comparison of the results with other research indicates that the use of the selected deep learning models and the suggested research methodology allows for improving the performance in detecting SQL injection vulnerabilities in Python-based web applications, with an F1 score reaching 0.90, which is approximately 10% higher than achieved by other researchers.