Web Phishing Classification using Combined Machine  Learning Methods

Bambang Mahardhika Poerbo Waseso; Noor Ageng Setiyanto

doi:10.33633/jcta.v1i1.8898

Web Phishing Classification using Combined Machine Learning Methods

Bambang Mahardhika Poerbo Waseso, Noor Ageng Setiyanto

Abstract

Phishing is a crime that uses social engineering techniques, both in deceptive statements and technically, to steal consumers' personal identification data and financial account credentials. With the new Phishing machine learning approach, websites can be recognized in real-time. K-Nearest Neighbor(KNN) and Naïve Bayes (NB) are popular machine learning approaches. KNN and NB have their own strengths and weaknesses. By combining the two, deficiencies can be covered. So this study proposes to combine K-Nearest Neighbor with Naïve Bayes to classify phishing websites. Based on the results of the accuracy test of the combination of KNN with k=8 and Naïve Bayes, a maximum accuracy of 93.44% is produced. This result is 6.25% superior compared to using only one classifier.

Keywords

Phishing detection, Phishing classification; Naïve Bases, K-Nearest Neighbor; Combined Classifier

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DOI: https://doi.org/10.33633/jcta.v1i1.8898

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