Efficient Ensemble-based Phishing Website Classification Models using Feature Importance Attribute Selection and Hyper parameter Tuning Approaches

Abstract

The internet is now a common place for different business, scientific and educational activities. However, there are bad elements in the internet space that keep using different attack techniques to perpetrate evils. Among these categories are people who use phishing techniques to launch attacks in the enterprise networks and internet space. The use of machine learning (ML) approaches for phishing attacks classification is an active research area in the field of cyber security. This is because phishing attack detection is a good example of intrusion identification tasks. These machine learning techniques can be categorized as single and ensemble learners. Ensemble learners have been identified to be more promising than the single classifiers. However, some of the ways to achieve an improved ML-based detection models are through feature selection/dimensionality reduction as well as hyper parameter tuning.  This study focuses on the classification of phishing websites using ensemble learning algorithms. Random Forest (RF) and Extra Trees ensembles were used for the phishing classification. The models built from the algorithms are optimized by applying a feature importance attribute selection and hyper parameter tuning approaches. The RF-based phishing classification model achieved 99.3% accuracy, 0.996 recall, 0.983 f1-score, 0.996 precision and 1.000 as AUC score. Similarly, Extra Trees-based model attained 99.1% accuracy, 0.990 as recall, F1-score was 0.981, precision of 0.990 while AUC score is 1.000. Thus, the RF-based phishing classification model slightly achieved better classification results when compared with the Extra Trees own. The study concluded that attribute selection and hyper parameter tuning approaches employed are very promising.