Phishing Website Detection Using A Stacked Hybrid Model With Explainable AI

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

An Explainable Ensemble Artificial Intelligence Framework for Real Time Phishing Website Detection that addresses the primary weakness of existing systems and is deployed as a real-time desktop application.

A Hybrid Intelligent Framework for Phishing Website Detection Using SVM and LightGBM

Phishing websites continue to pose a serious cybersecurity threat by deceiving users into revealing sensitive information such as login credentials, banking details, and personal data. Traditional blacklist-based detection techniques are ineffective against newly created phishing websites, necessitating intelligent machine learning solutions. This paper presents PhishShield, a hybrid phishing website detection framework that integrates Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM) to accurately classify legitimate and phishing websites. The proposed approach utilizes URL-based feature extraction and text preprocessing to generate meaningful representations for classification. SVM provides robust decision boundaries, while LightGBM enhances predictive performance through efficient gradient boosting. Experimental evaluation demonstrates that the hybrid framework achieves higher accuracy, precision, recall, and F1-score compared to conventional machine learning models. The system is implemented as a web-based application capable of real-time URL analysis, enabling users to identify malicious websites before accessing them. The proposed framework offers an efficient, scalable, and reliable solution for strengthening web security against evolving phishing attacks.

A Hybrid Machine Learning and Rule-Based Approach for Phishing Website Detection Using URL Features

Phishing attacks are considered one of the highest risks to cybersecurity, as they exploit users by pretending to be a valid site and gaining access to sensitive data. Traditional methods of detection, including blacklist-based systems, cannot uncover newly created phishing sites (zero-day attacks), need regular updates to their databases, and can generally not offer real-time protection. The paper suggests implementing a hybrid phishing site detection system involving machine learning with rule-based analysis to improve the accuracy and reliability of detection. The model proposed employs feature extraction using URLs, such as lexical and structural features like URL length, frequency of special characters, use of IP addresses, and occurrence of suspicious keywords. Three supervised machine learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression, where RF was indicated to achieve the highest performance. Moreover, a rule-based system of brand impersonation detection is also implemented to make the system resistant to targeted phishing attacks. Experimental findings prove that the hybrid methodology enhances classification accuracy and lowers false negatives compared to conventional ones. The proposed hybrid model represents an accuracy of 96%, with a recall of 94% and F1 of 95% ; it can be deployed in real-time and gives interpretable results by using confidence scoring. The solution suggested is a feasible and effective method of phishing detection and can be expanded to more sophisticated intelligent cybersecurity systems.

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.

Explainable Phishing Website Detection Using Comparative Machine Learning and SHAP

An integrated comparative evaluation that combines six-algorithm benchmarking, leakage-free hyperparameter optimization, and SHAP-based interpretation on a public phishing dataset, offering practical guidance for security analysts is offered.

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