Enhanced Phishing URLs Detection using Feature Selection and Machine Learning Approaches

Abstract

Phishing threats continue to compromise online security by using deceptive URLs to lure users and extract sensitive information. This paper presents a method for detecting phishing URLs that employs optimal feature selection techniques to improve detection system accuracy and efficiency. The proposed approach aims to enhance performance by identifying the most relevant features from a comprehensive set and applying various machine learning algorithms, including Decision Trees, XGBoost, Random Forest, Extra Trees, Logistic Regression, AdaBoost, and K-Nearest Neighbors. Key features are selected from an extensive feature set using techniques such as information gain, information gain ratio, and chi-square (χ2). Evaluation results indicate promising outcomes, with the potential to surpass existing methods. The Extra Trees classifier, combined with the chi-square feature selection method, achieved an accuracy, precision, recall, and F-measure of 98.23% using a subset of 28 features out of a total of 48. Integrating optimal feature selection not only reduces computational demands but also enhances the effectiveness of phishing URL detection systems.