Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering
Spammers have created a new kind of electronic mail (e-mail) called image-based spam to bypass text-based spam filters. Unfortunately, these images contain harmful links that can infect the user’s computer system and take a long time to be deleted, which can hamper users’ productivity and security....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/7/2/87 |
| _version_ | 1827738529013170176 |
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| author | Ghizlane Hnini Jamal Riffi Mohamed Adnane Mahraz Ali Yahyaouy Hamid Tairi |
| author_facet | Ghizlane Hnini Jamal Riffi Mohamed Adnane Mahraz Ali Yahyaouy Hamid Tairi |
| author_sort | Ghizlane Hnini |
| collection | DOAJ |
| description | Spammers have created a new kind of electronic mail (e-mail) called image-based spam to bypass text-based spam filters. Unfortunately, these images contain harmful links that can infect the user’s computer system and take a long time to be deleted, which can hamper users’ productivity and security. In this paper, a hybrid deep neural network architecture is suggested to address this problem. It is based on the convolution neural network (CNN), which has been enhanced with the convolutional block attention module (CBAM). Initially, CNN enhanced with CBAM is used to extract the most crucial information from each image-based e-mail. Then, the generated feature vectors are fed to the support vector machine (SVM) model to classify them as either spam or ham. Four datasets—including Image Spam Hunter (ISH), Annadatha, Chavda Approach 1, and Chavda Approach 2—are used in the experiments. The obtained results demonstrated that in terms of accuracy, our model exceeds the existing state-of-the-art methods. |
| first_indexed | 2024-03-11T02:46:41Z |
| format | Article |
| id | doaj.art-4fc4dc509d7f456cbebd77dd37748779 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-11T02:46:41Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-4fc4dc509d7f456cbebd77dd377487792023-11-18T09:18:34ZengMDPI AGBig Data and Cognitive Computing2504-22892023-05-01728710.3390/bdcc7020087Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam FilteringGhizlane Hnini0Jamal Riffi1Mohamed Adnane Mahraz2Ali Yahyaouy3Hamid Tairi4Laboratory of Computer Science, Signals, Automation and Cognitivism (LISAC), University Sidi Mohamed Ben Abdellah, Fez 30000, MoroccoLaboratory of Computer Science, Signals, Automation and Cognitivism (LISAC), University Sidi Mohamed Ben Abdellah, Fez 30000, MoroccoLaboratory of Computer Science, Signals, Automation and Cognitivism (LISAC), University Sidi Mohamed Ben Abdellah, Fez 30000, MoroccoLaboratory of Computer Science, Signals, Automation and Cognitivism (LISAC), University Sidi Mohamed Ben Abdellah, Fez 30000, MoroccoLaboratory of Computer Science, Signals, Automation and Cognitivism (LISAC), University Sidi Mohamed Ben Abdellah, Fez 30000, MoroccoSpammers have created a new kind of electronic mail (e-mail) called image-based spam to bypass text-based spam filters. Unfortunately, these images contain harmful links that can infect the user’s computer system and take a long time to be deleted, which can hamper users’ productivity and security. In this paper, a hybrid deep neural network architecture is suggested to address this problem. It is based on the convolution neural network (CNN), which has been enhanced with the convolutional block attention module (CBAM). Initially, CNN enhanced with CBAM is used to extract the most crucial information from each image-based e-mail. Then, the generated feature vectors are fed to the support vector machine (SVM) model to classify them as either spam or ham. Four datasets—including Image Spam Hunter (ISH), Annadatha, Chavda Approach 1, and Chavda Approach 2—are used in the experiments. The obtained results demonstrated that in terms of accuracy, our model exceeds the existing state-of-the-art methods.https://www.mdpi.com/2504-2289/7/2/87attention mechanismimage spamMobileNetV2convolutional block attention modulesupport vector machinedeep learning |
| spellingShingle | Ghizlane Hnini Jamal Riffi Mohamed Adnane Mahraz Ali Yahyaouy Hamid Tairi Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering Big Data and Cognitive Computing attention mechanism image spam MobileNetV2 convolutional block attention module support vector machine deep learning |
| title | Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering |
| title_full | Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering |
| title_fullStr | Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering |
| title_full_unstemmed | Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering |
| title_short | Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering |
| title_sort | attention mechanism and support vector machine for image based e mail spam filtering |
| topic | attention mechanism image spam MobileNetV2 convolutional block attention module support vector machine deep learning |
| url | https://www.mdpi.com/2504-2289/7/2/87 |
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