Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48
Technology is rising on daily basis with the advancement in web and artificial intelligence (AI), and big data developed by machines in various industries. All of these provide a gateway for cybercrimes that makes network security a challenging task. There are too many challenges in the development...
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| Format: | Article |
| Language: | English |
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Lublin University of Technology
2023-04-01
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| Series: | Advances in Sciences and Technology |
| Subjects: | |
| Online Access: | http://www.astrj.com/Ensemble-Model-for-Network-Intrusion-Detection-System-Based-on-Bagging-Using-J48,161820,0,2.html |
| _version_ | 1797857347382542336 |
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| author | Mohammad Mahmood Otoom Khalid Nazim Abdul Sattar Mutasim Al Sadig |
| author_facet | Mohammad Mahmood Otoom Khalid Nazim Abdul Sattar Mutasim Al Sadig |
| author_sort | Mohammad Mahmood Otoom |
| collection | DOAJ |
| description | Technology is rising on daily basis with the advancement in web and artificial intelligence (AI), and big data developed by machines in various industries. All of these provide a gateway for cybercrimes that makes network security a challenging task. There are too many challenges in the development of NID systems. Computer systems are becoming increasingly vulnerable to attack as a result of the rise in cybercrimes, the availability of vast amounts of data on the internet, and increased network connection. This is because creating a system with no vulnerability is not theoretically possible. In the previous studies, various approaches have been developed for the said issue each with its strengths and weaknesses. However, still there is a need for minimal variance and improved accuracy. To this end, this study proposes an ensemble model for the said issue. This model is based on Bagging with J48 Decision Tree. The proposed models outperform other employed models in terms of improving accuracy. The outcomes are assessed via accuracy, recall, precision, and f-measure. The overall average accuracy achieved by the proposed model is 83.73%. |
| first_indexed | 2024-04-09T20:55:18Z |
| format | Article |
| id | doaj.art-551691d8d67c46408a4ae3fb555b6c5a |
| institution | Directory Open Access Journal |
| issn | 2080-4075 2299-8624 |
| language | English |
| last_indexed | 2024-04-09T20:55:18Z |
| publishDate | 2023-04-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| series | Advances in Sciences and Technology |
| spelling | doaj.art-551691d8d67c46408a4ae3fb555b6c5a2023-03-29T19:58:36ZengLublin University of TechnologyAdvances in Sciences and Technology2080-40752299-86242023-04-0117232232910.12913/22998624/161820161820Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48Mohammad Mahmood Otoom0https://orcid.org/0000-0002-5818-9868Khalid Nazim Abdul Sattar1Mutasim Al Sadig2Department of Computer Science and Information, College of Science, Majmaah University, Al-Majmaah 11952, Saudi ArabiaDepartment of Computer Science and Information, College of Science, Majmaah University, Al-Majmaah 11952, Saudi ArabiaDepartment of Computer Science and Information, College of Science, Majmaah University, Al-Majmaah 11952, Saudi ArabiaTechnology is rising on daily basis with the advancement in web and artificial intelligence (AI), and big data developed by machines in various industries. All of these provide a gateway for cybercrimes that makes network security a challenging task. There are too many challenges in the development of NID systems. Computer systems are becoming increasingly vulnerable to attack as a result of the rise in cybercrimes, the availability of vast amounts of data on the internet, and increased network connection. This is because creating a system with no vulnerability is not theoretically possible. In the previous studies, various approaches have been developed for the said issue each with its strengths and weaknesses. However, still there is a need for minimal variance and improved accuracy. To this end, this study proposes an ensemble model for the said issue. This model is based on Bagging with J48 Decision Tree. The proposed models outperform other employed models in terms of improving accuracy. The outcomes are assessed via accuracy, recall, precision, and f-measure. The overall average accuracy achieved by the proposed model is 83.73%.http://www.astrj.com/Ensemble-Model-for-Network-Intrusion-Detection-System-Based-on-Bagging-Using-J48,161820,0,2.htmlmachine learningensemble learningcyber securitynetwork intrusion |
| spellingShingle | Mohammad Mahmood Otoom Khalid Nazim Abdul Sattar Mutasim Al Sadig Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48 Advances in Sciences and Technology machine learning ensemble learning cyber security network intrusion |
| title | Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48 |
| title_full | Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48 |
| title_fullStr | Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48 |
| title_full_unstemmed | Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48 |
| title_short | Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48 |
| title_sort | ensemble model for network intrusion detection system based on bagging using j48 |
| topic | machine learning ensemble learning cyber security network intrusion |
| url | http://www.astrj.com/Ensemble-Model-for-Network-Intrusion-Detection-System-Based-on-Bagging-Using-J48,161820,0,2.html |
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