Detection model for ambiguous intrusion using SMOTE and LSTM for Network Security
In today's interconnected world, networks play a crucial role. Consequently, network security has become increasingly vital. To ensure network security, various methods are employed, including digital signatures, firewalls, and intrusion detection. Among these methods, intrusion detection syste...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Publishing
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106131/ http://psasir.upm.edu.my/id/eprint/106131/1/document.pdf |
| _version_ | 1848864696723046400 |
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| author | Khalaf, Al-Ogaidi Ali Hameed Mohamed, Raihani Raziff, Abdul Rafiez Abdul |
| author_facet | Khalaf, Al-Ogaidi Ali Hameed Mohamed, Raihani Raziff, Abdul Rafiez Abdul |
| author_sort | Khalaf, Al-Ogaidi Ali Hameed |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In today's interconnected world, networks play a crucial role. Consequently, network security has become increasingly vital. To ensure network security, various methods are employed, including digital signatures, firewalls, and intrusion detection. Among these methods, intrusion detection systems have gained significant popularity due to their ability to identify new attacks. However, the accuracy of these systems still requires further improvement. One of the challenges is the potential bias introduced by using imbalance datasets that contains more information on normal activities than on attacks. To address it, SMOTE method was proposed and additionally, the study explores the use of Long Short-Term Memory (LSTM) for classification purposes. The experiments are conducted using two datasets: UNSW NB-15 and CICIDS 2017. The results obtained demonstrate that the proposed methods achieve an accuracy of 96 with the UNSW NB-15 dataset and 99 with the CICIDS 2017 dataset. These findings indicate an improvement of 3 and 1 respectively compared to existing literature. |
| first_indexed | 2025-11-15T13:52:55Z |
| format | Article |
| id | upm-106131 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T13:52:55Z |
| publishDate | 2024 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1061312024-10-08T08:13:24Z http://psasir.upm.edu.my/id/eprint/106131/ Detection model for ambiguous intrusion using SMOTE and LSTM for Network Security Khalaf, Al-Ogaidi Ali Hameed Mohamed, Raihani Raziff, Abdul Rafiez Abdul In today's interconnected world, networks play a crucial role. Consequently, network security has become increasingly vital. To ensure network security, various methods are employed, including digital signatures, firewalls, and intrusion detection. Among these methods, intrusion detection systems have gained significant popularity due to their ability to identify new attacks. However, the accuracy of these systems still requires further improvement. One of the challenges is the potential bias introduced by using imbalance datasets that contains more information on normal activities than on attacks. To address it, SMOTE method was proposed and additionally, the study explores the use of Long Short-Term Memory (LSTM) for classification purposes. The experiments are conducted using two datasets: UNSW NB-15 and CICIDS 2017. The results obtained demonstrate that the proposed methods achieve an accuracy of 96 with the UNSW NB-15 dataset and 99 with the CICIDS 2017 dataset. These findings indicate an improvement of 3 and 1 respectively compared to existing literature. Semarak Ilmu Publishing 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/106131/1/document.pdf Khalaf, Al-Ogaidi Ali Hameed and Mohamed, Raihani and Raziff, Abdul Rafiez Abdul (2024) Detection model for ambiguous intrusion using SMOTE and LSTM for Network Security. Journal of Advanced Research in Applied Sciences and Engineering Technology, 39 (2). pp. 191-203. ISSN 2462-1943 https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/3895 10.37934/araset.39.2.191203 |
| spellingShingle | Khalaf, Al-Ogaidi Ali Hameed Mohamed, Raihani Raziff, Abdul Rafiez Abdul Detection model for ambiguous intrusion using SMOTE and LSTM for Network Security |
| title | Detection model for ambiguous intrusion using SMOTE and LSTM for Network Security |
| title_full | Detection model for ambiguous intrusion using SMOTE and LSTM for Network Security |
| title_fullStr | Detection model for ambiguous intrusion using SMOTE and LSTM for Network Security |
| title_full_unstemmed | Detection model for ambiguous intrusion using SMOTE and LSTM for Network Security |
| title_short | Detection model for ambiguous intrusion using SMOTE and LSTM for Network Security |
| title_sort | detection model for ambiguous intrusion using smote and lstm for network security |
| url | http://psasir.upm.edu.my/id/eprint/106131/ http://psasir.upm.edu.my/id/eprint/106131/ http://psasir.upm.edu.my/id/eprint/106131/ http://psasir.upm.edu.my/id/eprint/106131/1/document.pdf |