Combatting Phishing Attacks: Leveraging Machine Learning for Real-Time Detection in Penetration Testing

Real-Time Phishing URL Detection Using a Hybrid Stacking Ensemble: Gradio and Browser Extension Deployment

Phishing attacks remain a prevalent and rapidly evolving cybersecurity threat, leveraging deceptive Uniform Resource Locators (URLs) and fraudulent websites to steal sensitive user data, financial credentials, and personal information. Traditional detection mechanisms, such as blacklist-based and heuristic approaches, struggle to mitigate zero-day phishing threats due to their reliance on static, manually updated databases. While machine learning and ensemble techniques have enhanced detection accuracy, existing literature predominantly focuses on offline evaluations using static datasets, offering limited support for real-time deployment, adaptability to evolving attack patterns, and continuous monitoring. To bridge this gap, this paper introduces a hybrid machine learning framework for real-time phishing website detection. The proposed architecture integrates an Artificial Neural Network (ANN) and a Bagging K-Nearest Neighbors (Bagging-KNN) classifier through a Logistic Regression-based stacking ensemble, combining their complementary learning capabilities to maximize classification performance while minimizing prediction error. Developed using the PhiUSIIL Phishing URL Dataset, the framework implements a leakage-free machine learning pipeline encompassing automated data cleaning, a train/test split performed prior to any preprocessing, feature engineering, StandardScaler-based normalization, SMOTE-based class balancing, and SelectKBest feature selection, all splitting-dependent steps fitted exclusively on the training partition. The framework’s efficacy is validated using Accuracy, Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix analysis. Beyond offline validation, the model is operationalized through two real-time deployment channels: a Gradio-based web interface for on-demand URL analysis, and a Chrome browser extension that automatically screens the active browser tab using a combination of rule-based checks, live queries to the deployed model, and a local heuristic fallback. By unifying ensemble learning, a leakage-conscious preprocessing pipeline, and dual real-time deployment tools, the proposed framework provides an effective, transparently evaluated solution for real-world phishing detection, while explicitly discussing the boundaries within which its strongest offline results should be interpreted.

Multi Model Approach for Phishing Website Detection using ML and DL Techniques

In the technology era, Phishing has continued to be a great challenge within the cybersecurity and web security landscape. This involves exploiting human trust on any online services and subtle technical flaws. This is to gather credentials, financial data, and sensitive information across diverse online platforms and various users. Traditional defenses like static blacklists, signature-based filters and simple detection rules are limited by slow update cycles and an inability to capture subtle syntactic and behavioral cues. To address these shortcomings, we propose a hybrid detection framework that fuses classical supervised machine-learning classifiers (e.g., Logistic Regression, SVM, Random Forest, XGBoost) with sequence-aware deep learning (LSTM) to jointly model lexical, structural, syntactic, and behavioral features extracted from URLs and webpage metadata. This combined approach leverages the interpretability and stability of ML models alongside the pattern-learning strength of LSTMs to detect both known and zero-day phishing attempts, produce calibrated confidence scores and deliver comprehensive reports via a real-time web interface resulting in a robust, transparent, and operationally useful solution for strengthening web security.

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.

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.

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

Browser-based phishing detection system using modern web technologies

A hybrid browser-resident phishing detection framework that combines three complementary detection mechanisms: a locally executed Random Forest model using URL lexical features, lightweight real-time DOM structure analysis, and VirusTotal’s multi-engine reputation service is proposed.