📅 08 August 2023
DOI: 10.33633/jcta.v1i1.8898
Web Phishing Classification using Combined Machine Learning Methods
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 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
ℹ️ Informasi Publikasi
Tanggal Publikasi
08 August 2023
Volume / Nomor / Tahun
Volume 1, Nomor 1, Tahun 2023
📝 HOW TO CITE
Waseso, Bambang Mahardhika Poerbo; Setiyanto, Noor Ageng, "Web Phishing Classification using Combined Machine Learning Methods," Journal of Computing Theories and Applications, vol. 1, no. 1, Aug. 2023.
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