Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm
Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number o...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9878104/ |
| _version_ | 1811204371088670720 |
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| author | Sanaa A. A. Ghaleb Mumtazimah Mohamad Waheed Ali H. M. Ghanem Abdullah B. Nasser Mohamed Ghetas Akibu Mahmoud Abdullahi Sami Abdulla Mohsen Saleh Humaira Arshad Abiodun Esther Omolara Oludare Isaac Abiodun |
| author_facet | Sanaa A. A. Ghaleb Mumtazimah Mohamad Waheed Ali H. M. Ghanem Abdullah B. Nasser Mohamed Ghetas Akibu Mahmoud Abdullahi Sami Abdulla Mohsen Saleh Humaira Arshad Abiodun Esther Omolara Oludare Isaac Abiodun |
| author_sort | Sanaa A. A. Ghaleb |
| collection | DOAJ |
| description | Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm’s search for ways to improve the SDS’s classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system’s performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively. |
| first_indexed | 2024-04-12T03:12:04Z |
| format | Article |
| id | doaj.art-4aa06dfce01048f58624d3d3ea335255 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T03:12:04Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4aa06dfce01048f58624d3d3ea3352552022-12-22T03:50:18ZengIEEEIEEE Access2169-35362022-01-0110984759848910.1109/ACCESS.2022.32045939878104Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization AlgorithmSanaa A. A. Ghaleb0https://orcid.org/0000-0003-4506-5214Mumtazimah Mohamad1Waheed Ali H. M. Ghanem2https://orcid.org/0000-0002-3764-4788Abdullah B. Nasser3Mohamed Ghetas4Akibu Mahmoud Abdullahi5Sami Abdulla Mohsen Saleh6Humaira Arshad7Abiodun Esther Omolara8Oludare Isaac Abiodun9Faculty of Engineering, University of Aden, Aden, YemenFaculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, MalaysiaFaculty of Engineering, University of Aden, Aden, YemenSchool of Technology and Innovation, University of Vaasa, Vaasa, FinlandFaculty of Computer Science, Nahda University, Beni Suef Governorate, EgyptFaculty of Computing and Informatics, Albukhary International University, Kedah, MalaysiaSchool of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang, MalaysiaDepartment of Computer Science, Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Computer Science, University of Abuja, Gwagwalada, NigeriaDepartment of Computer Science, University of Abuja, Gwagwalada, NigeriaNetworks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm’s search for ways to improve the SDS’s classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system’s performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively.https://ieeexplore.ieee.org/document/9878104/Spam detection system (SDS)grasshopper optimization algorithm (GOA)feature selection (FS)multi-objective optimization (MOO)multilayer perceptron (MLP) |
| spellingShingle | Sanaa A. A. Ghaleb Mumtazimah Mohamad Waheed Ali H. M. Ghanem Abdullah B. Nasser Mohamed Ghetas Akibu Mahmoud Abdullahi Sami Abdulla Mohsen Saleh Humaira Arshad Abiodun Esther Omolara Oludare Isaac Abiodun Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm IEEE Access Spam detection system (SDS) grasshopper optimization algorithm (GOA) feature selection (FS) multi-objective optimization (MOO) multilayer perceptron (MLP) |
| title | Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm |
| title_full | Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm |
| title_fullStr | Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm |
| title_full_unstemmed | Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm |
| title_short | Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm |
| title_sort | feature selection by multiobjective optimization application to spam detection system by neural networks and grasshopper optimization algorithm |
| topic | Spam detection system (SDS) grasshopper optimization algorithm (GOA) feature selection (FS) multi-objective optimization (MOO) multilayer perceptron (MLP) |
| url | https://ieeexplore.ieee.org/document/9878104/ |
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