SQL Injection is one of the top 10 vulnerabilities in web-based systems. This attack essentially penetrates the logical section of the database. If the database has a logical flaw, the attackers send a new type of logical payload and get all of the user's credentials. Despite the fact that technology has advanced significantly in recent years, SQL injections can still be carried out by taking advantage of security flaws. On the Kaggle SQL Injection Dataset, authors used multiple machine learning methods to identify and detect SQL Injection assaults, including Logistic Regression, AdaBoost (Adaptive Boosting), Random Forest, Naive Bayes, and XGBoost (Extreme Gradient Boosting) Classifier. According to this research, the best strategy for detecting SQL inject is Naive Bayes, which has an accuracy of 98.33, which is 2% better than previous work.

Purpose: Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases. However, the performance of these systems is adversely affected by the missing values in medical datasets. Imputation methods are used to predict these missing values. In this paper, a new imputation method called hybrid imputation optimized by the classifier (HIOC) is proposed to predict missing values efficiently.Design/methodology/approach: The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations (MICE), K nearest neighbor (KNN), mean and mode imputation methods in an optimum way. Performance of HIOC has been compared to MICE, KNN, and mean and mode methods. Four classifiers support vector machine (SVM), naive Bayes (NB), random forest (RF) and decision tree (DT) have been used to evaluate the performance of imputation methods.Findings: The results show that HIOC performed efficiently even with a high rate of missing values. It had reduced root mean square error (RMSE) up to 17.32% in the heart disease dataset and 34.73% in the breast cancer dataset. Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases. It increased classification accuracy up to 18.61% in the heart disease dataset and 6.20% in the breast cancer dataset.Originality/value: The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.