Heterogeneous Distributed Ensemble Feature Selection: An Enhancement Approach to Machine Learning for Phishing Detection

Phishing is a significant cybersecurity issue due to the rapid technological advancements facilitating it. Detecting these attacks ischallenging as the techniques continually evolve. While numerous strategies have been deployed, no single solution is foolproof.Machine learning is currently favoured for combating phishing, this particular method comprises several steps, with featureselection playing a critical step. The quality of the features selected in building the machine learning model plays a significant role.Traditional feature selection methods have limitations, such as determining a cutoff point and high computation. To overcomethese, a novel ensemble feature selection strategy was used, discarding correlated features and using a Borda count algorithm toenhance selection performance. Three filter-based predictors were used in the first phase, and the innovative HDEFS was appliedin the second phase, producing unique baseline webpage features. The results showed that models using HDEFS featuresimproved phishing detection. The bagged SVM model achieved the highest accuracy of 97.4%, outperforming other models. Thestudy suggests that the selection of optimal webpage features through the innovative proposed ensemble feature selectionapproach astronomical improves the performance of the phishing detection performance. Likewise, it produced efficient newfeatures different from outdated features such as IpAddress, AtSymbol, QueryLength, MissingTitle, NumQueryComponentspreviously used by the prior studies.