Semnan University International Journal of Nonlinear Analysis and Applications 2008-6822 14 8 2023 08 01 Comparison study for NLP using machine learning techniques to detecting SQL injection vulnerabilities 283 290 7473 10.22075/ijnaa.2022.28365.4098 EN Manar Hasan Ali AL-Maliki Computer Science Department, Informatics Institute for Postgraduate Studies, Iraq Mahdi Nsaif Jasim University of Information Technology and Communications, Iraq Journal Article 2022 06 13 Due to the vast number of electronic attacks that occur on a daily basis, protecting users' data is extremely important in this age of technology. Nowadays, cyber security is regarded as a top priority. Thus, the preservation of user privacy and data security is essential. The SQL vulnerability isn't a new form of website attack; it's been around for a long time. However, it is a new attack nowadays. ML algorithms were used to solve the problem of detecting SQL Injection attacks on websites. By training seven ML algorithms on a batch of data comprising SQL injection queries, including (Naive Bayes, Neural-Network, SVM, Random-Forest, KNN, and Logistic Regression) and choosing the best model that gives the highest accuracy. In comparison to previous studies, high-precision data were obtained, with the Naive-Bayes algorithm achieving 0.99 accuracies, 0.98 precision, 1.00 recall, and a 0.99 f1-score. In this paper, experiences, work schedules, and outcomes are examined. Compared to other methods, this naive Bayes approach has proven to be quite accurate in identifying SQL injection threats. https://ijnaa.semnan.ac.ir/article_7473_e17cf411885279d298e40f6beed78511.pdf