Phishing Attack Detection: An Improved Performance Through Ensemble Learning

Phishing is a cybercrime that deceives online users and steals their confidential information by impersonating a legitimate website or URL. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques utilize a set of features extracted from website samples, the structure and syntax of URLs, the content of the pages, and querying external resources. In this work, we use a dataset of 11,430 samples with 87 extracted features that are designed to be used as a benchmark for machine learning-based phishing detection systems. Our classification is based on an ensemble learning technique, which is further optimized using grid search. The experiments provide a detailed description of tuning the model's hyperparameters and its optimization. We evaluate the model using well-known evaluation metrics of accuracy, precision, recall, F1-score, and area under the ROC curve. The findings indicate that our optimized ensemble model classifies legitimate and phishing URLs with an accuracy of 95.36%, a precision of 96.29%, a recall of 94.24%, an F1 score of 95.26%, and the area under the ROC curve of 0.9876.