Pclf: Parallel cnn-lstm fusion model for sms spam filtering
Short Message Service (SMS) is widely used for its accessibility, simplicity, and cost-effectiveness in communication, bank notifications, and identity confirmation. The increase in spam text messages presents significant challenges, including time waste, potential financial scams, and annoyance for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00136.pdf |
| _version_ | 1797213264443080704 |
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| author | Reza Feizi Derakhshi Mohammad Zafarani-Moattar Elnaz Ala’a Al-Kabi Hussein Hashim Jawad Almarashy Ahmed |
| author_facet | Reza Feizi Derakhshi Mohammad Zafarani-Moattar Elnaz Ala’a Al-Kabi Hussein Hashim Jawad Almarashy Ahmed |
| author_sort | Reza Feizi Derakhshi Mohammad |
| collection | DOAJ |
| description | Short Message Service (SMS) is widely used for its accessibility, simplicity, and cost-effectiveness in communication, bank notifications, and identity confirmation. The increase in spam text messages presents significant challenges, including time waste, potential financial scams, and annoyance for users and carriers. This paper proposes a novel deep learning model based on parallel structure in the feature extraction step to address this challenge, unlike the traditional models that only enhance the classifier. This parallel model fuses local and temporal features to enhance feature representation by combining convolutional neural networks (CNN) and long short-term memory networks (LSTM). The performance of this model has been evaluated on the UCI SMS Collection V.1 dataset, which comprises both spam and ham messages. The model achieves an accuracy of 99.28% on this dataset. Also, the model demonstrates good precision, recall, and F1 score. This paper aims to provide the best protection from unwanted messages for mobile phone users. |
| first_indexed | 2024-04-24T10:55:31Z |
| format | Article |
| id | doaj.art-92cbd43b9d1e4fc99928f9bc71c056f0 |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-04-24T10:55:31Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-92cbd43b9d1e4fc99928f9bc71c056f02024-04-12T07:36:29ZengEDP SciencesBIO Web of Conferences2117-44582024-01-01970013610.1051/bioconf/20249700136bioconf_iscku2024_00136Pclf: Parallel cnn-lstm fusion model for sms spam filteringReza Feizi Derakhshi Mohammad0Zafarani-Moattar Elnaz1Ala’a Al-Kabi Hussein2Hashim Jawad Almarashy Ahmed3ComInSys Lab, Department of Computer Engineering, University of TabrizDepartment of Computer Engineering, Tabriz Branch, Islamic Azad UniversityMinistry of Education Iraq, General Direction Of Vocational EducationDecision support and information technology department, Karbala governmentShort Message Service (SMS) is widely used for its accessibility, simplicity, and cost-effectiveness in communication, bank notifications, and identity confirmation. The increase in spam text messages presents significant challenges, including time waste, potential financial scams, and annoyance for users and carriers. This paper proposes a novel deep learning model based on parallel structure in the feature extraction step to address this challenge, unlike the traditional models that only enhance the classifier. This parallel model fuses local and temporal features to enhance feature representation by combining convolutional neural networks (CNN) and long short-term memory networks (LSTM). The performance of this model has been evaluated on the UCI SMS Collection V.1 dataset, which comprises both spam and ham messages. The model achieves an accuracy of 99.28% on this dataset. Also, the model demonstrates good precision, recall, and F1 score. This paper aims to provide the best protection from unwanted messages for mobile phone users.https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00136.pdf |
| spellingShingle | Reza Feizi Derakhshi Mohammad Zafarani-Moattar Elnaz Ala’a Al-Kabi Hussein Hashim Jawad Almarashy Ahmed Pclf: Parallel cnn-lstm fusion model for sms spam filtering BIO Web of Conferences |
| title | Pclf: Parallel cnn-lstm fusion model for sms spam filtering |
| title_full | Pclf: Parallel cnn-lstm fusion model for sms spam filtering |
| title_fullStr | Pclf: Parallel cnn-lstm fusion model for sms spam filtering |
| title_full_unstemmed | Pclf: Parallel cnn-lstm fusion model for sms spam filtering |
| title_short | Pclf: Parallel cnn-lstm fusion model for sms spam filtering |
| title_sort | pclf parallel cnn lstm fusion model for sms spam filtering |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00136.pdf |
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