An innovative method to improve performance analysis in classification with accuracy of phishing websites using random forest algorithm by comparing with support vector machine algorithm - Vallepu, Rambabu; Karunakaran, Malathi (AIP conference proceedings, 2023)
To Improve performance analysis in classification with accuracy of innovative phishing websites using the innovative Random Forest algorithm by comparing with the Support Vector Machine algorithm. Random Forest algorithm (N=20) is compared with the Support Vector Machine algorithm (N=20) in order to get high accuracy. The framework depends on Machine Learning. Random forest has the highest accuracy (92.11%) in comparison to the Support Vector Machine algorithm (90.26%) and the independent T-test was carried out and shows that it is statistically insignificant (α=0.354) with a confidence value of 95%. Random Forest algorithm obtained seems to be better accuracy than the Support Vector Machine algorithm in the detection of phishing Websites.