Phishing URLs Detection Using Naives Baiyes, Random Forest and LightGBM Algorithms - Foozy, Cik Feresa Mohd; Anuar, Muhammad Amir Izaan; Maslan, Andi; Adam, Husaini Aza Mohd; Mahdin, Hairulnizam (International Journal of Data Science, 2024)
In response to the increasing complexity of phishing attacks, particularly in Malaysia, this study aims to compare the accuracy and precision effectiveness of three machine learning algorithms Naive Bayes, Random Forest and LightGBM in detecting URL (Uniform Resource Locator) phishing. This research employs a comprehensive four-stages methodology including data collection, preprocessing, feature selection, and classification to analyze data for URL phishing attacks classification. The objectives are to identify phishing attack features based on dataset using and machine learning algorithms, to compare between three classification algorithms of Naïve Bayes, Random Forest, and Light Gradient Boosting Model (LightGBM), and to evaluate the model in terms of accuracy, and precision using machine learning algorithms. Through this comparative analysis, the study seeks to develop a phishing detection model, to identify the suitable features and classification algorithms for the datasets. The result accuracy, precision for NB, Random & LightGBM. The Accuracy result of Naives Baiyes is 94.24%, the result of Random Forest is 94.80% and the result of LightGBM is 95.00%.