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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Performa : Jurnal Manajemen dan Start-up Bisnis IJIIS: International Journal of Informatics and Information Systems International Journal for Applied Information Management Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Izumi, Calvina
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Journal : International Journal for Applied Information Management

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Uncovering the Efficiency of Phishing Detection: An In-depth Comparative Examination of Classification Algorithms Sugianto, Dwi; Putawa, Rilliandi Arindra; Izumi, Calvina; Ghaffar, Soeltan Abdul
International Journal for Applied Information Management Vol. 4 No. 1 (2024): Regular Issue: April 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i1.72

Abstract

This research aims to investigate the potential security risks associated with phishing email attacks and compare the performance of three main classification algorithms: random forest, SVM, and a combination of k-fold cross-validation with the xgboost model. The dataset consists of 18,634 emails, with 7,312 identified as phishing emails and 11,322 considered safe. Through experiments, the combination of k-fold cross-validation and xgboost demonstrated the best performance with the highest accuracy of 0.9712828770799785. The email classification graph provides a visual insight into the distribution of classification results, aiding in understanding patterns and trends in phishing attack detection. The analysis of the ROC curve results indicates that k-fold cross-validation and xgboost have a higher AUC compared to random forest and SVM, signifying a better ability to predict the correct class. The conclusion emphasizes the importance of the combination of k-fold cross-validation and xgboost in enhancing email security, with the potential for increased accuracy through parameter adjustments.
Co-Authors Ghaffar, Soeltan Abdul Prambudi, Niko Lugas Putawa, Rilliandi Arindra Setiawan, Wilbert Clarence Sugianto, Dwi Taqwa Hariguna