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.


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    Titel :

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


    Beteiligte:
    Rambabu, Vallepu (Autor:in) / Malathi, K. (Autor:in) / Mahaveerakannan, R (Autor:in)


    Erscheinungsdatum :

    01.12.2022


    Format / Umfang :

    721172 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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