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PhishDetect: A ranking-based classifier integration approach for improving phishing website detection.

Ekta Gandotra¹, Deepak Gupta¹, Meghna Dhalaria¹

¹Department of Computer Science & Engineering and Information Technology, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India.

Scientific Reports

|June 25, 2026

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This summary is machine-generated.

This study introduces an improved machine learning method for detecting phishing websites, significantly enhancing accuracy over traditional techniques. The new approach achieves a higher weighted F-measure, offering a more reliable solution against increasing cyber threats.

Area of Science:

Cybersecurity
Machine Learning
Data Science

Background:

Phishing attacks are escalating with increased internet usage, exploiting users for personal information.
Conventional machine learning and ensemble methods show limitations, including high false positive/negative rates in phishing detection.
There is a critical need for more reliable solutions to identify phishing webpages.

Purpose of the Study:

To enhance phishing detection accuracy by developing a novel approach that combines base classifiers.
To improve upon existing machine learning and ensemble learning methods for identifying malicious websites.
To validate the proposed method's effectiveness and generalization capabilities using benchmark datasets.

Main Methods:

A new phishing detection model is proposed, combining base classifiers using ranking schemes based on prediction errors.

Keywords:

Classifier ranking Classifiers integration Cybersecurity Ensemble learning Machine learning Phishing detection

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The approach's effectiveness was evaluated using a standard benchmark dataset.

Further validation was performed using an additional benchmark dataset to assess generalization.

Main Results:

The proposed approach achieved a weighted F-measure of 0.984, outperforming traditional methods.
Compared to stacking all classifiers (0.970) and top three classifiers (0.974), the proposed method showed superior performance.
Experimental results confirmed the approach's validity and generalization capability.

Conclusions:

The developed method offers a more accurate and reliable solution for phishing webpage detection compared to existing techniques.
The novel combination of base classifiers using prediction error ranking schemes significantly improves detection rates.
This research contributes to advancing cybersecurity defenses against sophisticated phishing attacks.