Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection
SMS spam is a pervasive issue that affects millions worldwide, leading to significant inconvenience, time wastage, and potential financial scams. Given the prevalence and potential harm, accurate and real-time detection of SMS spam is crucial. This paper proposes a novel approach to SMS spam detecti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10296923/ |
| _version_ | 1797632795754889216 |
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| author | Hussein Alaa Al-Kabbi Mohammad-Reza Feizi-Derakhshi Saeid Pashazadeh |
| author_facet | Hussein Alaa Al-Kabbi Mohammad-Reza Feizi-Derakhshi Saeid Pashazadeh |
| author_sort | Hussein Alaa Al-Kabbi |
| collection | DOAJ |
| description | SMS spam is a pervasive issue that affects millions worldwide, leading to significant inconvenience, time wastage, and potential financial scams. Given the prevalence and potential harm, accurate and real-time detection of SMS spam is crucial. This paper proposes a novel approach to SMS spam detection involving five steps: preprocessing, feature extraction, feature fusion, feature selection, and classification. Our model is designed to simultaneously capture local, temporal, and global text message features using a hybrid deep learning model to enhance feature representation. We evaluated our model using the UCI dataset, comparing it with traditional and deep learning algorithms such as RF and BERT using cross-validation to ensure the robustness of our results. Our proposed method exhibited superior performance, achieving a good accuracy of 99.56%, surpassing other methods. The effectiveness of this method in SMS spam detection proved its potential for real-world implementation, where it could substantially mitigate the prevalence and impact of SMS spam. |
| first_indexed | 2024-03-11T11:42:51Z |
| format | Article |
| id | doaj.art-c068487d0cef472ab5f1412957425cea |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T11:42:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c068487d0cef472ab5f1412957425cea2023-11-10T00:00:59ZengIEEEIEEE Access2169-35362023-01-011112375612376510.1109/ACCESS.2023.332789710296923Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam DetectionHussein Alaa Al-Kabbi0https://orcid.org/0000-0001-9230-7947Mohammad-Reza Feizi-Derakhshi1https://orcid.org/0000-0002-8548-976XSaeid Pashazadeh2https://orcid.org/0000-0002-8949-9180Department of Computer Engineering, Computerized Intelligence Systems Laboratory, University of Tabriz, Tabriz, IranDepartment of Computer Engineering, Computerized Intelligence Systems Laboratory, University of Tabriz, Tabriz, IranDepartment of Computer Engineering, University of Tabriz, Tabriz, IranSMS spam is a pervasive issue that affects millions worldwide, leading to significant inconvenience, time wastage, and potential financial scams. Given the prevalence and potential harm, accurate and real-time detection of SMS spam is crucial. This paper proposes a novel approach to SMS spam detection involving five steps: preprocessing, feature extraction, feature fusion, feature selection, and classification. Our model is designed to simultaneously capture local, temporal, and global text message features using a hybrid deep learning model to enhance feature representation. We evaluated our model using the UCI dataset, comparing it with traditional and deep learning algorithms such as RF and BERT using cross-validation to ensure the robustness of our results. Our proposed method exhibited superior performance, achieving a good accuracy of 99.56%, surpassing other methods. The effectiveness of this method in SMS spam detection proved its potential for real-world implementation, where it could substantially mitigate the prevalence and impact of SMS spam.https://ieeexplore.ieee.org/document/10296923/CNNdata fusiondeep learningLSTMSMS spam detection |
| spellingShingle | Hussein Alaa Al-Kabbi Mohammad-Reza Feizi-Derakhshi Saeid Pashazadeh Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection IEEE Access CNN data fusion deep learning LSTM SMS spam detection |
| title | Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection |
| title_full | Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection |
| title_fullStr | Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection |
| title_full_unstemmed | Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection |
| title_short | Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection |
| title_sort | multi type feature extraction and early fusion framework for sms spam detection |
| topic | CNN data fusion deep learning LSTM SMS spam detection |
| url | https://ieeexplore.ieee.org/document/10296923/ |
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