Phishing image spam classification research trends: Survey and open issues
A phishing email is an attack that focused completely on people to circumvent existing traditional security algorithms. The email appears to be a dependable, appropriate, and solid communication medium for internet users. At present, the email is submerged with spam content, both in text-based form...
| Main Authors: | , , , , |
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| Format: | Article |
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The Science and Information Organization
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/87151/ |
| _version_ | 1848860373578416128 |
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| author | John Abari, Ovye Mohd Sani, Nor Fazlida Khalid, Fatimah Mohd Yunus Bin Sharum, Mohd Yunus Mohd Ariffin, Noor Afiza |
| author_facet | John Abari, Ovye Mohd Sani, Nor Fazlida Khalid, Fatimah Mohd Yunus Bin Sharum, Mohd Yunus Mohd Ariffin, Noor Afiza |
| author_sort | John Abari, Ovye |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | A phishing email is an attack that focused completely on people to circumvent existing traditional security algorithms. The email appears to be a dependable, appropriate, and solid communication medium for internet users. At present, the email is submerged with spam content, both in text-based form or undesired text planted inside the images. This study reviews articles on phishing image spam classification published from 2006 to 2020 based on spam classification application domains, datasets, features sets, spam classification methods, and the measurement metrics adopted in the existing studies. More than 50 articles, both from Web of Science and Scopus databases were picked. Achieving the study’s target, we carried out a broad survey and analysis to identify the domains where spam classification was applied. Furthermore, several public data sets, features set, classification methods, and measuring metrics are found and the popular once were pinpointed. The study revealed that Personal Collection, Dredze, and Spam Archives datasets are the most commonly used datasets in image spam classification research. Low-level and image metadata are the most widely used features set. The methods of image spam classification as identified in this study are supervised machine learning, unsupervised machine learning, semi-supervised machine learning, content-based and statistical learning. Among these methods, the most commonly utilized is the Support Vector Machine (SVM) which falls under supervised machine learning. This is followed by Na¨ıve Bayes and K-Nearest Neighbor. The commonly adopted metrics for the performance evaluation of the existing image spam classifiers are also identified and briefly discussed. We compared the performance of the state-of-the-art image spam models. Lastly, we pointed out promising directions for future research. |
| first_indexed | 2025-11-15T12:44:12Z |
| format | Article |
| id | upm-87151 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T12:44:12Z |
| publishDate | 2020 |
| publisher | The Science and Information Organization |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-871512024-05-17T04:14:03Z http://psasir.upm.edu.my/id/eprint/87151/ Phishing image spam classification research trends: Survey and open issues John Abari, Ovye Mohd Sani, Nor Fazlida Khalid, Fatimah Mohd Yunus Bin Sharum, Mohd Yunus Mohd Ariffin, Noor Afiza A phishing email is an attack that focused completely on people to circumvent existing traditional security algorithms. The email appears to be a dependable, appropriate, and solid communication medium for internet users. At present, the email is submerged with spam content, both in text-based form or undesired text planted inside the images. This study reviews articles on phishing image spam classification published from 2006 to 2020 based on spam classification application domains, datasets, features sets, spam classification methods, and the measurement metrics adopted in the existing studies. More than 50 articles, both from Web of Science and Scopus databases were picked. Achieving the study’s target, we carried out a broad survey and analysis to identify the domains where spam classification was applied. Furthermore, several public data sets, features set, classification methods, and measuring metrics are found and the popular once were pinpointed. The study revealed that Personal Collection, Dredze, and Spam Archives datasets are the most commonly used datasets in image spam classification research. Low-level and image metadata are the most widely used features set. The methods of image spam classification as identified in this study are supervised machine learning, unsupervised machine learning, semi-supervised machine learning, content-based and statistical learning. Among these methods, the most commonly utilized is the Support Vector Machine (SVM) which falls under supervised machine learning. This is followed by Na¨ıve Bayes and K-Nearest Neighbor. The commonly adopted metrics for the performance evaluation of the existing image spam classifiers are also identified and briefly discussed. We compared the performance of the state-of-the-art image spam models. Lastly, we pointed out promising directions for future research. The Science and Information Organization 2020 Article PeerReviewed John Abari, Ovye and Mohd Sani, Nor Fazlida and Khalid, Fatimah and Mohd Yunus Bin Sharum, Mohd Yunus and Mohd Ariffin, Noor Afiza (2020) Phishing image spam classification research trends: Survey and open issues. International Journal of Advanced Computer Science and Applications, 11 (11). 794 - 805. ISSN 2156-5570; ESSN: 2158-107X https://thesai.org/Publications/ViewPaper?Volume=11&Issue=11&Code=IJACSA&SerialNo=96 10.14569/ijacsa.2020.0111196 |
| spellingShingle | John Abari, Ovye Mohd Sani, Nor Fazlida Khalid, Fatimah Mohd Yunus Bin Sharum, Mohd Yunus Mohd Ariffin, Noor Afiza Phishing image spam classification research trends: Survey and open issues |
| title | Phishing image spam classification research trends: Survey and open issues |
| title_full | Phishing image spam classification research trends: Survey and open issues |
| title_fullStr | Phishing image spam classification research trends: Survey and open issues |
| title_full_unstemmed | Phishing image spam classification research trends: Survey and open issues |
| title_short | Phishing image spam classification research trends: Survey and open issues |
| title_sort | phishing image spam classification research trends: survey and open issues |
| url | http://psasir.upm.edu.my/id/eprint/87151/ http://psasir.upm.edu.my/id/eprint/87151/ http://psasir.upm.edu.my/id/eprint/87151/ |