Identifying the Most Effective Feature Category in Machine Learning-based Phishing Website Detection
This paper proposes an improved approach to categorise phishing features into precise categories. Existing features are surveyed from the current phishing detection works and grouped according to the improved categorisation approach. The performances of various feature sets are evaluated using the C...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Publishing Corporation
2018
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/25776/1/Identifying%20the%20Most%20Effective%20Feature%20Category%20in%20Machine%20Learning-based%20Phishing%20Website%20Detection%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/25776/ https://www.sciencepubco.com/index.php/ijet/article/view/23331 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | This paper proposes an improved approach to categorise phishing features into precise categories. Existing features are surveyed from the current phishing detection works and grouped according to the improved categorisation approach. The performances of various feature sets are evaluated using the C4.5 classifier, whereby the content URL obfuscation category is found to perform the best, achieving an
accuracy of 95.97%. Additional benchmarking is conducted to compare the performance of the winning feature set against other feature sets utilised in existing phishing detection techniques. Results suggest that the winning feature set is indeed an effective feature category which has contributed significantly to the performance of existing machine learning-based phishing detection systems. |
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