An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection
The use of short message service (SMS) and e-mail have increased too much over the last decades. 80% of people do not read e-mails while 98% of cell phone users daily read their SMS. However, these communication media are unsafe and can produce malicious attacks called spam. The e-mails that pretend...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2023/6648970 |
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| _version_ | 1832559732180647936 |
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| author | Umair Maqsood Saif Ur Rehman Tariq Ali Khalid Mahmood Tahani Alsaedi Mahwish Kundi |
| author_facet | Umair Maqsood Saif Ur Rehman Tariq Ali Khalid Mahmood Tahani Alsaedi Mahwish Kundi |
| author_sort | Umair Maqsood |
| collection | DOAJ |
| description | The use of short message service (SMS) and e-mail have increased too much over the last decades. 80% of people do not read e-mails while 98% of cell phone users daily read their SMS. However, these communication media are unsafe and can produce malicious attacks called spam. The e-mails that pretend to be from a trusted company to provide “financial or personal information” are phishing e-mails. These e-mails contain some links; users might download malicious software on their computers when they click on them. Most techniques and models are developed to automatically detect these “SMS and e-mails” but none of them achieved 100% accuracy. In previous studies using machine learning (ML), spam detection using a small dataset has resulted in lower accuracy. To counter this problem, in this paper, multiple classifiers of ML and a classifier of deep learning (DL) were applied to the SMS and e-mail dataset for spam detection with higher accuracy. After conducting experiments on the real dataset, the researchers concluded that the proposed system performed better and more accurately than previously existing models. Specifically, the support vector machine (SVM) classifier outperformed all others. These results suggest that SVM is the optimal choice for classification purposes. |
| format | Article |
| id | doaj-art-eb43b3bf844447c9a4bc339b0594ce25 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-eb43b3bf844447c9a4bc339b0594ce252025-02-03T01:29:26ZengWileyApplied Computational Intelligence and Soft Computing1687-97322023-01-01202310.1155/2023/6648970An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam DetectionUmair Maqsood0Saif Ur Rehman1Tariq Ali2Khalid Mahmood3Tahani Alsaedi4Mahwish Kundi5University Institute of Information TechnologyUniversity Institute of Information TechnologyUniversity Institute of Information TechnologyInstitute of Computing and Information TechnologyApplied CollegeUniversity of LeicesterThe use of short message service (SMS) and e-mail have increased too much over the last decades. 80% of people do not read e-mails while 98% of cell phone users daily read their SMS. However, these communication media are unsafe and can produce malicious attacks called spam. The e-mails that pretend to be from a trusted company to provide “financial or personal information” are phishing e-mails. These e-mails contain some links; users might download malicious software on their computers when they click on them. Most techniques and models are developed to automatically detect these “SMS and e-mails” but none of them achieved 100% accuracy. In previous studies using machine learning (ML), spam detection using a small dataset has resulted in lower accuracy. To counter this problem, in this paper, multiple classifiers of ML and a classifier of deep learning (DL) were applied to the SMS and e-mail dataset for spam detection with higher accuracy. After conducting experiments on the real dataset, the researchers concluded that the proposed system performed better and more accurately than previously existing models. Specifically, the support vector machine (SVM) classifier outperformed all others. These results suggest that SVM is the optimal choice for classification purposes.http://dx.doi.org/10.1155/2023/6648970 |
| spellingShingle | Umair Maqsood Saif Ur Rehman Tariq Ali Khalid Mahmood Tahani Alsaedi Mahwish Kundi An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection Applied Computational Intelligence and Soft Computing |
| title | An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection |
| title_full | An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection |
| title_fullStr | An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection |
| title_full_unstemmed | An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection |
| title_short | An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection |
| title_sort | intelligent framework based on deep learning for sms and e mail spam detection |
| url | http://dx.doi.org/10.1155/2023/6648970 |
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