An Explainable Ensemble Artificial Intelligence Framework for Real-Time Phishing Website Detection

Phishing Website Detection Using A Stacked Hybrid Model With Explainable AI

A Phishing Website Detection System Using a Stacked Hybrid Model With Explainable AI, designed to accurately classify websites as phishing or legitimate while providing transparent decision explanations, is presented.

Improved Phishing Email Detection through Hybrid Machine Learning and AI-Based Data Augmentation

Phishing attacks continue to evolve in sophistication, making them one of the most significant cybersecurity threats affecting individuals and organizations. Traditional phishing detection techniques based on rule-based systems and standalone machine learning algorithms often struggle to identify modern phishing emails generated using artificial intelligence and advanced social engineering techniques. This study proposes an AI-driven hybrid ensemble model that integrates Support Vector Machine (SVM), Random Forest (RF), and Bidirectional Long Short-Term Memory (Bi-LSTM) to improve phishing email detection accuracy and robustness. To address dataset imbalance and the limited availability of AI-generated phishing emails, an AI-based data augmentation module was incorporated to generate realistic synthetic phishing samples during model training. The proposed methodology consists of data collection, preprocessing, feature engineering, synthetic data generation, individual model training, and ensemble classification using a soft voting mechanism. Experimental evaluation was performed using benchmark phishing email datasets and standard performance metrics, including accuracy, precision, recall, F1-score, confusion matrix, and inference latency. Results indicate that the proposed hybrid model achieved an overall detection accuracy of 98.9%, outperforming a comparable existing model, which achieved 95.2% accuracy. Furthermore, the hybrid approach demonstrated improved generalization, reduced false classifications, and faster detection latency. The findings suggest that combining classical machine learning, deep learning, and AI-driven data augmentation provides an effective framework for protecting modern email systems against both conventional and AI-generated phishing attacks.

Phishing Website Detection using Artificial Neural Networks and Count Vectorization: A Web-Deployed Approach

Enhancing Phishing Website Detection Using Artificial Neural Network with Logic Gate-Based Feature Interaction Modeling

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.

An Explainable Hybrid Model for Real-Time Phishing Detection Using Multi-Source Features

External validation against ten official Iraqi university websites resulted in correct classification with no false positives, demonstrating that the proposed phishing detection model is reliable.

A CNN–BiLSTM Hybrid Model for Email Phishing Detection

Email phishing is by far one of the most common and harmful cybersecurity risks that make use of misguided communication methods to breach sensitive user data. Phishing attacks keep changing, which greatly constrains the performance of conventional rule-based and classical machine-learning methods of detection. The proposed study will introduce a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-term Memory (Bi-LSTM) networks to detect emails with high phishing accuracy. The model proposed examines various elements of email, such as, email headers, subject lines, message bodies and embedded URLs, in the form of a rich feature representation that represents structural and contextual features. The datasets collected in the real world, PhishTank and Mendeley repositories, were preprocessed by feature binning, redundancy reduction as well as class balancing to improve the quality of data and generalization. The experimental findings indicate that CNNBILSTM model has a high accuracy (96), precision (95.9), recall (96) and ROC-AUC (0.99), which is better than a number of baseline models, including Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN) and individual neural networks. The statistical analysis also verifies that the performance improvements are substantial (p < 0.05) and the confidence intervals are not wide which means the study is very consistent and reliable. Based on the findings, it is possible to note that the CNN-BiLSTM model represents a reliable, scalable, and high-performance solution that can be applied to real-time email phishing detection in the contemporary cybersecurity settings.