A hybrid super learner ensemble for phishing detection on mobile devices
Abstract In today’s digital age, the rapid increase in online users and massive network traffic has made ensuring security more challenging. Among the various cyber threats, phishing remains one of the most significant. Phishing is a cyberattack in which attackers steal sensitive information, such a...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02009-8 |
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| author | Routhu Srinivasa Rao Cheemaladinne Kondaiah Alwyn Roshan Pais Bumshik Lee |
| author_facet | Routhu Srinivasa Rao Cheemaladinne Kondaiah Alwyn Roshan Pais Bumshik Lee |
| author_sort | Routhu Srinivasa Rao |
| collection | DOAJ |
| description | Abstract In today’s digital age, the rapid increase in online users and massive network traffic has made ensuring security more challenging. Among the various cyber threats, phishing remains one of the most significant. Phishing is a cyberattack in which attackers steal sensitive information, such as usernames, passwords, and credit card details, through fake web pages designed to mimic legitimate websites. These attacks primarily occur via emails or websites. Several antiphishing techniques, such as blacklist-based, source code analysis, and visual similarity-based methods, have been developed to counter phishing websites. However, these methods have specific limitations, including vulnerability to zero-day attacks, susceptibility to drive-by-downloads, and high detection latency. Furthermore, many of these techniques are unsuitable for mobile devices, which face additional constraints, such as limited RAM, smaller screen sizes, and lower computational power. To address these limitations, this paper proposes a novel hybrid super learner ensemble model named Phish-Jam, a mobile application specifically designed for phishing detection on mobile devices. Phish-Jam utilizes a super learner ensemble that combines predictions from diverse Machine Learning (ML) algorithms to classify legitimate and phishing websites. By focusing on extracting features from URLs, including handcrafted features, transformer-based text embeddings, and other Deep Learning (DL) architectures, the proposed model offers several advantages: fast computation, language independence, and robustness against accidental malware downloads. From the experimental analysis, it is observed that the super learner ensemble achieved significant accuracy of 98.93%, precision of 99.15%, MCC of 97.81% and F1 Score of 99.07%. |
| format | Article |
| id | doaj-art-9f5705fa53aa4260956d095b9a6efcaa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-9f5705fa53aa4260956d095b9a6efcaa2025-08-20T03:53:57ZengNature PortfolioScientific Reports2045-23222025-05-0115111710.1038/s41598-025-02009-8A hybrid super learner ensemble for phishing detection on mobile devicesRouthu Srinivasa Rao0Cheemaladinne Kondaiah1Alwyn Roshan Pais2Bumshik Lee3Department of Computer Science and Engineering, Gandhi Institute of Technology and ManagementInformation Security Research Lab, Department of Computer Science and Engineering, National Institute of Technology KarnatakaInformation Security Research Lab, Department of Computer Science and Engineering, National Institute of Technology KarnatakaDepartment of Information and Communication Engineering, Chosun UniversityAbstract In today’s digital age, the rapid increase in online users and massive network traffic has made ensuring security more challenging. Among the various cyber threats, phishing remains one of the most significant. Phishing is a cyberattack in which attackers steal sensitive information, such as usernames, passwords, and credit card details, through fake web pages designed to mimic legitimate websites. These attacks primarily occur via emails or websites. Several antiphishing techniques, such as blacklist-based, source code analysis, and visual similarity-based methods, have been developed to counter phishing websites. However, these methods have specific limitations, including vulnerability to zero-day attacks, susceptibility to drive-by-downloads, and high detection latency. Furthermore, many of these techniques are unsuitable for mobile devices, which face additional constraints, such as limited RAM, smaller screen sizes, and lower computational power. To address these limitations, this paper proposes a novel hybrid super learner ensemble model named Phish-Jam, a mobile application specifically designed for phishing detection on mobile devices. Phish-Jam utilizes a super learner ensemble that combines predictions from diverse Machine Learning (ML) algorithms to classify legitimate and phishing websites. By focusing on extracting features from URLs, including handcrafted features, transformer-based text embeddings, and other Deep Learning (DL) architectures, the proposed model offers several advantages: fast computation, language independence, and robustness against accidental malware downloads. From the experimental analysis, it is observed that the super learner ensemble achieved significant accuracy of 98.93%, precision of 99.15%, MCC of 97.81% and F1 Score of 99.07%.https://doi.org/10.1038/s41598-025-02009-8Phishing URLsSuper learner ensemblePhish-JamText embedding transformersDeep learningMachine learning |
| spellingShingle | Routhu Srinivasa Rao Cheemaladinne Kondaiah Alwyn Roshan Pais Bumshik Lee A hybrid super learner ensemble for phishing detection on mobile devices Scientific Reports Phishing URLs Super learner ensemble Phish-Jam Text embedding transformers Deep learning Machine learning |
| title | A hybrid super learner ensemble for phishing detection on mobile devices |
| title_full | A hybrid super learner ensemble for phishing detection on mobile devices |
| title_fullStr | A hybrid super learner ensemble for phishing detection on mobile devices |
| title_full_unstemmed | A hybrid super learner ensemble for phishing detection on mobile devices |
| title_short | A hybrid super learner ensemble for phishing detection on mobile devices |
| title_sort | hybrid super learner ensemble for phishing detection on mobile devices |
| topic | Phishing URLs Super learner ensemble Phish-Jam Text embedding transformers Deep learning Machine learning |
| url | https://doi.org/10.1038/s41598-025-02009-8 |
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