comparison study for nlp using machine learning techniques to detecting sql injection vulnerabilities

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.