Estimation of accuracy in novel reinforcement learning with random forest for prediction of SQL injection vulnerability.

Improved prediction of SQL injection vulnerabilities using a comparison of F1-Measure in a Random Forest model and Novel Reinforcement learning. Accuracy in Outcomes and Random Forest's Reinforcement Learning Innovations (83.8580 percent). There are a total of 55,336 samples to be analysed, split evenly between two groups. With an accuracy of 83.8580 percent, the reinforcement learning algorithm outperforms the support Random Forest algorithm's 74.5890 percent accuracy by a statistically significant amount (P0.004). The results of this research show that when it comes to predicting SQL injection vulnerabilities, the Novel Reinforcement learning Algorithm performs significantly better than the Random Forest algorithm. [ABSTRACT FROM AUTHOR]