PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914960218521600 |
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| author | Ovi, Md Sultanul Islam Rahman, Md. Hasibur Hossain, Mohammad Arif |
| author_facet | Ovi, Md Sultanul Islam Rahman, Md. Hasibur Hossain, Mohammad Arif |
| contents | Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy. Through advanced feature selection methods such as SelectKBest and RFECV, and optimizations like hyperparameter tuning and data balancing, the model was trained and evaluated on four publicly available datasets. PhishGuard outperformed state-of-the-art models, achieving a detection accuracy of 99.05% on one of the datasets, with similarly high results across other datasets. This research demonstrates that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_19825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection Ovi, Md Sultanul Islam Rahman, Md. Hasibur Hossain, Mohammad Arif Cryptography and Security Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy. Through advanced feature selection methods such as SelectKBest and RFECV, and optimizations like hyperparameter tuning and data balancing, the model was trained and evaluated on four publicly available datasets. PhishGuard outperformed state-of-the-art models, achieving a detection accuracy of 99.05% on one of the datasets, with similarly high results across other datasets. This research demonstrates that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance. |
| title | PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2409.19825 |