SVM driven approach for detecting DoS attacks in SDN environment
Software-Defined Networking (SDN) reveals a significant progression in networking technology, offering improved management and operational oversight of network infrastructures. Even though the control plane offers benefits, it is still susceptible to Denial of Service (DoS) attacks, and this poses a...
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| Format: | Article |
| Language: | en |
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EDP Sciences
2025
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| Online Access: | http://ir.unimas.my/id/eprint/51137/1/SVM%20driven.pdf http://ir.unimas.my/id/eprint/51137/ https://www.ijsmdo.org/articles/smdo/full_html/2025/01/smdo250265/smdo250265.html https://doi.org/10.1051/smdo/2025031 |
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| _version_ | 1854094204338176000 |
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| author | Najmun, Nisa Adnan Shahid, Khan Azman, Bujang Masli Nusrat, Shaheen |
| author_facet | Najmun, Nisa Adnan Shahid, Khan Azman, Bujang Masli Nusrat, Shaheen |
| author_sort | Najmun, Nisa |
| building | Centre for Academic Information Services (CAIS) |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Sarawak |
| content_source | UNIMAS Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Software-Defined Networking (SDN) reveals a significant progression in networking technology, offering improved management and operational oversight of network infrastructures. Even though the control plane offers benefits, it is still susceptible to Denial of Service (DoS) attacks, and this poses a significant threat to
system security. By taking advantage of the network’s centralized architecture, these attacks pose serious
dangers and can overload controllers, leading to severe packet loss and significant downtime in the network. To
address this challenge, we propose a novel approach that efficiently detects DoS attacks by implementing a
packet inspection process using a queuing mechanism, followed by machine learning classification using SVM
and KNN algorithms. These algorithms were rigorously evaluated using the CICDoS 2017 dataset and integrated into an SDN threat-detection framework. The results of extensive testing in SDN environment demonstrated higher efficiency measures, such as enhanced network performance by reducing latency and resource consumption, maintaining a false-positive rate under 5%, and achieving a detection accuracy of 99%. These results demonstrate how well our proposed approach works to successfully detect DoS attacks in SDN
systems. Moreover, the novel approach, the thorough end-to-end solution exhibited, and the importance of the
experimental outcomes all work together to establish a solid basis for future studies in this area. |
| format | Article |
| id | my.unimas.ir-51137 |
| institution | Universiti Malaysia Sarawak |
| language | en |
| publishDate | 2025 |
| publisher | EDP Sciences |
| record_format | eprints |
| spelling | my.unimas.ir-511372025-12-29T07:54:53Z http://ir.unimas.my/id/eprint/51137/ SVM driven approach for detecting DoS attacks in SDN environment Najmun, Nisa Adnan Shahid, Khan Azman, Bujang Masli Nusrat, Shaheen QA75 Electronic computers. Computer science Software-Defined Networking (SDN) reveals a significant progression in networking technology, offering improved management and operational oversight of network infrastructures. Even though the control plane offers benefits, it is still susceptible to Denial of Service (DoS) attacks, and this poses a significant threat to system security. By taking advantage of the network’s centralized architecture, these attacks pose serious dangers and can overload controllers, leading to severe packet loss and significant downtime in the network. To address this challenge, we propose a novel approach that efficiently detects DoS attacks by implementing a packet inspection process using a queuing mechanism, followed by machine learning classification using SVM and KNN algorithms. These algorithms were rigorously evaluated using the CICDoS 2017 dataset and integrated into an SDN threat-detection framework. The results of extensive testing in SDN environment demonstrated higher efficiency measures, such as enhanced network performance by reducing latency and resource consumption, maintaining a false-positive rate under 5%, and achieving a detection accuracy of 99%. These results demonstrate how well our proposed approach works to successfully detect DoS attacks in SDN systems. Moreover, the novel approach, the thorough end-to-end solution exhibited, and the importance of the experimental outcomes all work together to establish a solid basis for future studies in this area. EDP Sciences 2025 Article PeerReviewed text en http://ir.unimas.my/id/eprint/51137/1/SVM%20driven.pdf Najmun, Nisa and Adnan Shahid, Khan and Azman, Bujang Masli and Nusrat, Shaheen (2025) SVM driven approach for detecting DoS attacks in SDN environment. International Journal for Simulation and Multidisciplinary Design Optimization (IJSMDO), 16 (29). pp. 1-19. ISSN 1779-6288 https://www.ijsmdo.org/articles/smdo/full_html/2025/01/smdo250265/smdo250265.html https://doi.org/10.1051/smdo/2025031 |
| spellingShingle | QA75 Electronic computers. Computer science Najmun, Nisa Adnan Shahid, Khan Azman, Bujang Masli Nusrat, Shaheen SVM driven approach for detecting DoS attacks in SDN environment |
| title | SVM driven approach for detecting DoS attacks in SDN
environment |
| title_full | SVM driven approach for detecting DoS attacks in SDN
environment |
| title_fullStr | SVM driven approach for detecting DoS attacks in SDN
environment |
| title_full_unstemmed | SVM driven approach for detecting DoS attacks in SDN
environment |
| title_short | SVM driven approach for detecting DoS attacks in SDN
environment |
| title_sort | svm driven approach for detecting dos attacks in sdn
environment |
| topic | QA75 Electronic computers. Computer science |
| url | http://ir.unimas.my/id/eprint/51137/1/SVM%20driven.pdf http://ir.unimas.my/id/eprint/51137/ https://www.ijsmdo.org/articles/smdo/full_html/2025/01/smdo250265/smdo250265.html https://doi.org/10.1051/smdo/2025031 |
| url_provider | http://ir.unimas.my/ |