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Enhanced Phishing Detection: An Ensemble Stacking Model with DT-RFECV and SMOTE |
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PP: 1481-1493 |
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doi:10.18576/amis/180624
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Author(s) |
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Mangayarkarasi Ramaiah,
Vanmathi Chandrasekaran,
Vikash Chand,
Asokan Vasudevan,
Suleiman Ibrahim Mohamma,
Eddie Eu Hui Soon,
Qusai Shambour,
Muhammad Turki Alshurideh,
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Abstract |
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| Phishing websites are a significant threat, constantly evolving to deceive users into revealing sensitive information. While current anti-phishing systems rely on URLs, website content, and third-party data, they often struggle to keep pace with these dynamic scams. This study addresses these challenges by introducing a novel approach that analyzes the effectiveness of URL-based features, JavaScript characteristics, and anomaly-based indicators in detecting malicious web links. To overcome the issues of data imbalance and feature selection, our approach incorporates SMOTE oversampling and a Decision Tree-Recursive Feature Elimination cross- validation (DT-RFECV) wrapper method. The selected features are then used to train an ensemble stacking model that combines Decision Trees, Random Forests, and Bagging. The framework was rigorously evaluated on two benchmarking datasets and achieved impressive accuracy rates of 97.7% on Dataset-1 and 97.5% on Dataset-2 using ten features, underscoring the effectiveness of our approach. Our proposed framework significantly contributes to the internet community’s defense against phishing scams with its unique features, ensemble model construction, and promising results.
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