Implementasi Metode Hybrid Genetic Algorithm dan Random Forest untuk Deteksi Dini Tautan Phishing
Abstract
The evolving nature of phishing attacks requires rapid and accurate detection. However, high-dimensional URL attributes can increase computational overhead during real-time processing. This study proposes a hybrid approach combining Genetic Algorithm (GA) and Random Forest (RF) for web-based phishing detection. Using 235,749 samples from the PhiUSIIL Phishing URL dataset, GA selects 20 features from the original 47 attributes, combining lexical URL characteristics with lightweight web content indicators. The dataset was partitioned using an 80:20 stratified split, yielding 188,599 training samples and 47,150 independent test samples. Evaluation on the test set yielded an accuracy of 99.66%, precision of 99.63%, recall of 99.78%, and F1-score of 99.70%, with 60 false negatives and 100 false positives. Stability testing using 10 different random seeds for data partitioning produced an average accuracy of 99.65%, with a standard deviation of 0.0241%. The model was integrated into a two-layer defense architecture using Flask and Vue.js, achieving a local server response time of less than 25 milliseconds.
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Program Studi Teknik Informatika, Fakuktas Sains Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakuktas Sains Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakuktas Sains Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakuktas Sains Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
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Total: 23 References- Ahammad, I., Rahman, M. S., & Islam, M. R. (2023). URL obfuscation techniques and feature selection in machine learning-based phishing detection: A review. Journal of Information Security and Applications, 75, Article 103480.
- Alani, M. M., & Tawfik, A. I. (2022). Rule-based and blacklist approaches in phishing detection: Limitations and future directions. Cybersecurity, 5(1), 12–25.
- Al-Haimi, B., Sjarif, N. N. A., & Yuhaniz, A. Z. (2022). Lexical features-based detection of phishing URLs using machine learning techniques: A systematic review. IEEE Access, 10, 32145–32160.
- Al-Jumah, A., Al-Khulaifi, A., & Bamasag, O. (2023). Lightweight lexical feature selection for real-time phishing URL detection using Random Forest. Computers & Security, 128, Article 103150.
- Al-Sarem, M., Saeed, F., Boulila, W., & Al-Mohaifer, A. (2022). Feature selection and ensemble learning algorithms for detecting phishing websites. IEEE Access, 10, 56342–56357.
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