An Innovative Method to Predict the Accuracy of Phishing Websites by Comparing Logistic Regression Algorithm with Support Vector Machine Algorithm

Abstract

To compare novel LR with the SVM technique to estimate the precision of phishing websites. Materials and Methods: The SVM method's algorithm for supervised learning (N = 20) is compared to the Logistic Regression algorithm's supervised learning algorithm (N = 20). To achieve great precision, the G power value is set to 0.8. Machine Learning is used in the framework. Compared to the SVM approach, LR has more precision (92.00%). (90.26%). With a confidence value of 95%, the impartial T-Test was run (p =.375), indicating the importance score that is statistically insignificant (p>0.05). Conclusion: The LR approach appeared to detect phishing websites with greater accuracy than the SVM technique.