<![CDATA[Despite advances in machine learning-based phishing detection, existing Artificial Neural Network (ANN) models operate as black boxes with no interpretable explanation of feature interactions—a critical limitation for security analysts. Furthermore, most approaches deploy large feature sets without investigating whether a minimal subset achieves equivalent performance. This study develops a phishing detection system combining ANN with Logic Gate-Based Feature Interaction Modeling (LGFIM), a novel framework that characterizes ANN decisions through AND, OR, and XOR Boolean operations, addressing both accuracy and interpretability gaps. Using the PhiUSIIL dataset (235,795 instances), Pearson correlation identified URLSimilarityIndex (r=0.8604) and HasSocialNet (r=0.7843) as the two most discriminative features. An ANN (2-64-32-16-1, ReLU, Adam) trained on an 80/20 split achieved 99.63% accuracy, 100% recall, 99.68% F1-score, and 99.91% AUC-ROC with zero false negatives. The LGFIM analysis reveals the classification boundary follows a predominantly AND-type Boolean structure: the AND gate achieves 99.67% accuracy against true labels, while ANN predictions align with AND for 42.48% of samples and XOR for 57.52%, together accounting for 100% of all predictions. This is the first study to comprehensively characterize ANN phishing decisions through logic gate interaction patterns, providing a zero-cost interpretability layer for cybersecurity operations.]]>
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