Generalized Explainable AI Framework for Phishing Detection on Heterogeneous Textual Data

Phishing remains one of the most pervasive cybersecurity threats, exploiting humanand technical vulnerabilities and targeting users through deceptive Emails,URLs, and SMS messages. Artificial Intelligence (AI) and Machine Learning (ML)techniques have been widely used to improve phishing detection accuracy. However,most existing studies have focused on specific data types, thereby limitingthe scope of their applicability, and lacking a generalized framework integratingheterogeneous data sources within phishing context. In this study, we propose ageneralized phishing detection framework that leverages classical machine learning(Random Forest and Logistic Regression) and deep learning (ConvolutionalNeural Network) to identify phishing attempts across heterogeneous textual data,such as Emails, URLs, and SMS messages. Moreover, we integrate interpretabilityinto model decisions using Explainable AI, particularly SHapley Additive exPlanations(SHAP), to enhance transparency and trustworthiness. The framework isevaluated based on both predictive performance and inference efficiency. Experimentalresults show that Random Forest achieves the highest accuracy (93%)and F1-score (85%), highlighting the efficiency of the classifier on tabular data forthe binary classification task at hand, while SHAP local and global explanationsreveal semantically relevant features influencing model decisions, where wordssuch as “admin” and “login” are identified as strong phishing indicators. These resultsdemonstrate the promise of our unified, interpretable approach in advancingadaptive and trustworthy generalized phishing detection systems.