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PhishNet 1.0: optuna-optimized stacking ensemble with Boruta-based feature selection for phishing URL detection.

Achin Jain¹, Shakir Khan², Kashish Koli¹

¹Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.

Scientific Reports

|December 6, 2025

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This summary is machine-generated.

This study enhances phishing detection using ensemble learning and metaheuristic algorithms. The optimized stacking classifier achieved 96.15% accuracy, offering a reliable cybersecurity approach.

Area of Science:

Cybersecurity
Machine Learning
Data Science

Background:

Phishing attacks pose a significant threat to internet users and organizations.
Effective detection of phishing websites is crucial for cybersecurity.
Existing methods require enhancement for improved accuracy and reliability.

Purpose of the Study:

To improve phishing detection rates by integrating ensemble learning with metaheuristic algorithms.
To evaluate the performance of various classifiers and ensemble techniques for phishing URL detection.
To optimize the hyperparameters of ensemble models using advanced metaheuristic algorithms.

Main Methods:

Feature selection using the Boruta method.
Evaluation of classifiers including Logistic Regression, KNN, SVM, Decision Tree, Naïve Bayes, and Gradient Boosting.

Keywords:

Boruta Ensemble learning Logistic regression Machine learning Optimization Phishing detection Stacking

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Implementation of ensemble methods: Soft Voting and Stacking.

Hyperparameter optimization of the stacking model using metaheuristic algorithms (GA, ACO, PSO, Bayesian Optimization, Optuna).

Main Results:

Gradient Boosting, KNN, and Decision Tree showed high performance among individual classifiers.
The stacking ensemble model with Logistic Regression as the final estimator outperformed other estimators.
The Optuna-optimized stacking classifier achieved the highest accuracy (96.15%), precision (96.45%), recall (96.68%), and F1 score (96.56%).

Conclusions:

Ensemble learning combined with metaheuristic optimization significantly enhances phishing webpage detection.
The proposed integrated framework, PhishNet 1.0, offers a novel and effective approach for real-world cybersecurity.
The study establishes a reproducible benchmark for phishing detection in cybersecurity applications.