An Innovative Method to Predict the Accuracy of Phishing Websites by Comparing Logistic Regression Algorithm with Support Vector Machine Algorithm
Vallepu Rambabu, K. Malathi, R Mahaveerakannan · 2022 6th International Conference on Electronics, Communication and Aerospace Technology · 2022
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.