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Improving the phishing website detection using empirical analysis of Function Tree and its variants.
Abdullateef O Balogun1, Kayode S Adewole1, Muiz O Raheem1
1Department of Computer Science, University of Ilorin, Ilorin, Nigeria.
Heliyon
|July 19, 2021
Summary
This study introduces Functional Tree (FT) based meta-learning models to detect phishing websites, significantly improving accuracy and reducing false positives compared to existing methods.
Area of Science:
- Cybersecurity
- Machine Learning
- Data Science
Background:
- Phishing attacks pose a significant threat to internet users and website owners, causing financial losses and eroding trust.
- Existing anti-phishing methods like blacklists and traditional machine learning models have limitations in detecting new threats and maintaining high accuracy.
Purpose of the Study:
- To propose and evaluate Functional Tree (FT) based meta-learning models for enhanced phishing website detection.
- To investigate the empirical analysis of FT and its variants for improving the accuracy of phishing detection systems.
Main Methods:
- Development of Functional Tree (FT) based meta-learning models.
- Empirical analysis and comparison with baseline classifiers, meta-learners, and hybrid models for phishing detection.
Main Results:
- The proposed FT based meta-learners achieved high detection accuracy of 98.51%.
- The models demonstrated a low false positive rate of 0.015.
- FT based models outperformed existing methods in detecting both legitimate and phishing websites.
Conclusions:
- Functional Tree (FT) based meta-learning models offer a highly effective solution for detecting phishing websites.
- The adoption of FT and its meta-learner variants is recommended for cybersecurity applications to combat phishing and related attacks.
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