DOI: https://doi.org/10.14710/transmisi.26.3.114-121

ENHANCING BANK CUSTOMER PROTECTION AGAINST PHISHING ATTACKS THROUGH XGBOOST-BASED FEATURE ANALYSIS

Tan Regina Karin  -  Informatics, Faculty of Informatics, Universitas Dian Nuswantoro, Indonesia
*Ramadhan Rakhmat Sani  -  Informatics, Faculty of Informatics, Universitas Dian Nuswantoro, Indonesia
Farrikh Alzami scopus  -  Informatics, Faculty of Informatics, Universitas Dian Nuswantoro, Indonesia
Asih Rohmani  -  Informatics, Faculty of Informatics, Universitas Dian Nuswantoro, Indonesia
Dikirim: 26 Jun 2024; Diterbitkan: 4 Nov 2024.
Akses Terbuka Copyright (c) 2024 Transmisi: Jurnal Ilmiah Teknik Elektro under http://creativecommons.org/licenses/by-sa/4.0.

Citation Format:
Sari

Internet usage in Indonesia has significantly increased, with approximately 175.4 million people or 64% of the population actively using the internet. While the internet provides numerous benefits, such as easy access to information and faster communication, this rise in usage also opens opportunities for cybercriminals to exploit user vulnerabilities. One of the most common forms of cybercrime is phishing, which attempts to steal users' personal information by impersonating a trusted entity. Current methods for detecting phishing are ineffective against zero-day phishing attacks. Therefore, this study employs the XGBoost algorithm to detect phishing websites. The results show that the XGBoost model, using feature selection techniques, can enhance phishing detection accuracy to 95.5%, with a precision of 95.5%, recall of 95.1%, and F1-score of 95.3%. With these capabilities, XGBoost can be used to protect internet users from evolving phishing threats and assist banks in anticipating customer losses.

Fulltext View|Download
Kata Kunci: bank customer, feature selection, internet usage, phishing website, zero-day phishing attacks, XGBoost algorithm

Article Metrics:

Article Info
Bagian: Artikel - Teknologi Informasi dan Komputer
Bahasa : EN
Recent articles
ENHANCING BANK CUSTOMER PROTECTION AGAINST PHISHING ATTACKS THROUGH XGBOOST-BASED FEATURE ANALYSIS IMPLEMENTASI DEVOPS PADA PENGEMBANGAN APLIKASI ANDROID PENDETEKSI KUALITAS BERAS BERBASIS MACHINE LEARNING PERANCANGAN DAN PEMBUATAN PROTOTYPE WATER SPRAY PEMBERSIH PANEL SURYA BERBASIS INTERNET OF THINGS ANALISIS PENGUKURAN HARMONISA TEGANGAN DAN ARUS LISTRIK DI GEDUNG ADMINISTRASI UNIVERSITAS JAYABAYA ANALISIS PENGUKURAN KAPASITOR BANK DENGAN TUNED REACTOR PADA GEDUNG ATRIA RESIDENCES GADING SERPONG RANCANG BANGUN ALAT PERAGA TRAINER FUZZY LOGIC CONTROLLER PADA KECEPATAN MOTOR DC BERBASIS ARDUINO DAN LABVIEW RANCANG BANGUN TEKNOLOGI TEPAT GUNA MONITORING PENGGUNAAN MULTI DISPENSER AIR OTOMATIS BERBASIS INTERNET OF THINGS DENGAN MODE OPERASI GANDA TERAPAN More recent articles
Most cited articles
PERANCANGAN MAXIMUM POWER POINT TRACKING PANEL SURYA MENGGUNAKAN BUCK BOOST CONVERTER DENGAN METODE INCREMENTAL CONDUCTANCE Aplikasi Pengenalan Ucapan dengan Ekstraksi Mel-Frequency Cepstrum Coefficients (MFCC) Melalui Jaringan Syaraf Tiruan (JST) Learning Vector Quantization (LVQ) untuk Mengoperasikan Kursor Komputer ANALISIS JARINGAN FTTH (FIBER TO THE HOME) BERTEKNOLOGI GPON (GIGABIT PASSIVE OPTICAL NETWORK) Alat Pelacak Lokasi Berbasis GPS Via Komunikasi Seluler Analisis Indikasi Kegagalan Transformator dengan Metode Dissolved Gas Analysis More cited articles
  1. . A. Susilo Yuda Irawan, N. Heryana, H. Siti Hopipah, D. Rahma Putri, and J. Hs Ronggo Waluyo Puseurjaya Telukjambe Timur Karawang Jawa Barat, “Identifikasi Website Phishing dengan Perbandingan Algoritma Klasifikasi,” 2021. [Online]. Available: www.phishtank.com
  2. . M. Jonathan, S. Rostianingsih, and H. Novianus Palit, “Pengaruh Feature Selection terhadap Kinerja C5.0, XGBoost, dan Random Forest dalam Mengklasifikasikan Website Phishing,” 2022
  3. . N. B. Putri and A. W. Wijayanto, “Analisis Komparasi Algoritma Klasifikasi Data Mining Dalam Klasifikasi Website Phishing,” Komputika : Jurnal Sistem Komputer, vol. 11, no. 1, pp. 59–66, Jan. 2022, doi: 10.34010/komputika.v11i1.4350
  4. . R. Ester, S. Lina, and M. Sitio, “OPTIMASI ALGORITMA KLASIFIKASI DECISION TREE (CART) DENGAN METODE BAGGING UNTUK DETEKSI WEBSITE PHISHING,” 2024. [Online]. Available: http://ojsamik.amikmitragama.ac.id
  5. . A. Aljofey et al., “An effective detection approach for phishing websites using URL and HTML features,” Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-10841-5
  6. . L. Tang and Q. H. Mahmoud, “A Survey of Machine Learning-Based Solutions for Phishing Website Detection,” Machine Learning and Knowledge Extraction, vol. 3, no. 3. MDPI, pp. 672–694, Sep. 01, 2021. doi: 10.3390/make3030034
  7. . A. P. Rosyadi, W. Maharani, and P. H. Gani, “PERSONALITY DETECTION ON TWITTER USER USING XGBOOST ALGORITHM,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 1, pp. 69–75, 2024, doi: 10.52436/1.jutif.2024.5.1.1166
  8. . Jan Melvin Ayu Soraya Dachi and Pardomuan Sitompul, “Analisis Perbandingan Algoritma XGBoost dan Algoritma Random Forest Ensemble Learning pada Klasifikasi Keputusan Kredit,” JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM, vol. 2, no. 2, pp. 87–103, Oct. 2023, doi: 10.55606/jurrimipa.v2i2.1336
  9. . Nasution M, Rohmat Saedudin R, and Widartha V, “PERBANDINGAN AKURASI ALGORITMA NAÏVE BAYES DAN ALGORITMA XGBOOST PADA KLASIFIKASI PENYAKIT DIABETES,” vol. 8, no. 5, pp. 9765–9772, Oct. 2021
  10. . L. Mat Rani, C. F. Mohd Foozy, and S. N. B. Mustafa, “Feature Selection to Enhance Phishing Website Detection Based On URL Using Machine Learning Techniques,” Journal of Soft Computing and Data Mining, vol. 4, no. 1, pp. 30–41, May 2023, doi: 10.30880/jscdm.2023.04.01.003
  11. . T. Z. Difaizi, O. P.-W. L. Camille, T. C. Benhura, and G. Gupta, “URL Based Malicious Activity Detection Using Machine Learning,” in 2023 International Conference on Disruptive Technologies (ICDT), IEEE, May 2023, pp. 414–418. doi: 10.1109/ICDT57929.2023.10150899
  12. . Y. Li and W. Chen, “A Comparative Performance Assessment of Ensemble Learning for Credit Scoring,” Mathematics, vol. 8, no. 10, p. 1756, Oct. 2020, doi: 10.3390/math8101756
  13. . S. Das Guptta, K. T. Shahriar, H. Alqahtani, D. Alsalman, and I. H. Sarker, “Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques,” Annals of Data Science, vol. 11, no. 1, pp. 217–242, Feb. 2024, doi: 10.1007/s40745-022-00379-8
  14. . I. T. Julianto, D. Kurniadi, M. R. Nashrulloh, A. Mulyani, and J. I. Komputer, “COMPARISON OF CLASSIFICATION ALGORITHM AND FEATURE SELECTION IN BITCOIN SENTIMENT ANALYSIS,” Jurnal Teknik Informatika (JUTIF), vol. 3, no. 3, 2022, doi: 10.20884/1.jutif.2022.3.3.343
  15. . X. Xiong, X. Guo, P. Zeng, R. Zou, and X. Wang, “A Short-Term Wind Power Forecast Method via XGBoost Hyper-Parameters Optimization,” Front Energy Res, vol. 10, May 2022, doi: 10.3389/fenrg.2022.905155
  16. . K. Riehl, M. Neunteufel, and M. Hemberg, “Hierarchical confusion matrix for classification performance evaluation,” Jun. 2023, doi: 10.1093/jrsssc/qlad057
  17. . Kurnia D, Mazdadi M, Kartini D, Nugroho R, and Abadi F, “SELEKSI FITUR DENGAN PARTICLE SWARM OPTIMIZATION PADA KLASIFIKASI PENYAKIT PARKINSON MENGGUNAKAN XGBOOST,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 10, no. 5, pp. 1083–1094, Oct. 2023

Last update:

No citation recorded.

Last update: 2024-11-22 06:59:09

No citation recorded.