EnLeM: ensemble learning-based model to detect phishing websites

Phishing involves manipulating individuals into revealing private data, e.g., user IDs, bank details, and passwords. The observed surge in fraud is related to increased deception, impersonation, and advanced online attacks. Thus, effective phishing detection methods are required to mitigate escalating global phishing threats. Existing methods (e.g., heuristics-based, signature-based, and visual similarity-based methods) attempt to detect phishing sites, and machine learning (ML) and deep learning (DL) methods are effective in the cybersecurity context in terms of learning from data, offering insights, and forecasting. However, independent ML algorithms are limited when handling complex data, and DL techniques surpass traditional ML methods in terms of performance but require more data and time. To tackle these challenges, we present EnLeM, an ensemble learning model designed specifically for phishing website detection. EnLeM brings together three well-known machine learning classifiers—decision tree, random forest, and k-nearest neighbor—using a hard voting mechanism, and further strengthens efficiency with Mutual Information–based feature selection. When tested on the UCI phishing dataset, EnLeM delivered strong results, reaching 97.21% accuracy and a 97.51% F1-score. Compared to individual ML classifiers, it consistently performed better, and it also proved more efficient than deep learning models such as CNN and LSTM. Notably, EnLeM maintained stable accuracy across different feature subsets while cutting execution time by roughly 13%. By striking a balance between accuracy, speed, and interpretability, EnLeM stands out as a practical and scalable solution for real-time phishing detection without the heavy resource demands of deep learning approaches.