Explainability Measurement of Machine Learning Model in Phishing Detection - Abdullah Fajar (International Journal of Intelligent Systems and Applications in Engineering, 2025)
Explainability in phishing detection models can enhance phishing assault mitigation by fostering confidence and elucidating the detection process. The essential requirements for facilitating human comprehension and assessment of the reasons a specific URL is deemed insecure for visitation. The aims of this study are to investigate some machine learning models in phishing detection which have abilities to fulfil the critical needs of explanation using explainability metric. This study applies a methodology starting with dataset collection of phishing and legitimate URL as the sources of various features. Then the models selected, which are often known have good quality in classification between phishing or legitimate label. The modeling results are processed using an explainer method to generate a comprehensive understanding of feature behaviors that influence model predictions. Instead of present accuracy metric results only, this study discusses how explainability metric shows how the features contribute to the model. The conclusion shows that some features have abilities to influence the model decision in general or specifically, then how the features contribute to the model in terms of stability and distribution behaviors. The study shows that some features that may be identified as key features of model behavior then can be applied practically to phishing detection systems such as firewall or SIEM (Security Information and Event Management).