A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments
Software Defined Networking (SDN) threats make network components vulnerable to cyber-attacks, creating obstacles for new model development that necessitate innovative security countermeasures, like Intrusion Detection Systems (IDSs). The centralized SDN controller, which has global view and control...
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| Format: | Article |
| Language: | English |
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D. G. Pylarinos
2024-04-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6756 |
| _version_ | 1797226291881050112 |
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| author | Harman Yousif Ibrahim Khalid Najla Badie Ibrahim Aldabagh |
| author_facet | Harman Yousif Ibrahim Khalid Najla Badie Ibrahim Aldabagh |
| author_sort | Harman Yousif Ibrahim Khalid |
| collection | DOAJ |
| description | Software Defined Networking (SDN) threats make network components vulnerable to cyber-attacks, creating obstacles for new model development that necessitate innovative security countermeasures, like Intrusion Detection Systems (IDSs). The centralized SDN controller, which has global view and control over the whole network and the availability of processing and storing capabilities, makes the deployment of artificial intelligence-based IDS in controllers a hot topic in the research community to resolve security issues. In order to develop effective AI-based IDSs in an SDN environment, there must be a high-quality dataset for training the model to offer effective and accurate attack prediction. There are some intrusion detection datasets used by researchers, but those datasets are either outdated or incompatible with the SDN environment. In this survey, an overview of the published work was conducted using the InSDN dataset from 2020 to 2023. Also, research challenges and future work for further research on IDS issues when deployed in an SDN environment are discussed, particularly when employing machine learning and deep learning models. Moreover, possible solutions for each issue are provided to help the researchers carry out and develop new methods of secure SDN.
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| first_indexed | 2024-04-24T14:22:35Z |
| format | Article |
| id | doaj.art-70aaaced7c7a46f9b12f547ecc4039a9 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-04-24T14:22:35Z |
| publishDate | 2024-04-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-70aaaced7c7a46f9b12f547ecc4039a92024-04-03T06:14:20ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362024-04-0114210.48084/etasr.6756A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking EnvironmentsHarman Yousif Ibrahim Khalid0Najla Badie Ibrahim AldabaghCollege of Science, University of Duhok, Kurdistan Region, IraqSoftware Defined Networking (SDN) threats make network components vulnerable to cyber-attacks, creating obstacles for new model development that necessitate innovative security countermeasures, like Intrusion Detection Systems (IDSs). The centralized SDN controller, which has global view and control over the whole network and the availability of processing and storing capabilities, makes the deployment of artificial intelligence-based IDS in controllers a hot topic in the research community to resolve security issues. In order to develop effective AI-based IDSs in an SDN environment, there must be a high-quality dataset for training the model to offer effective and accurate attack prediction. There are some intrusion detection datasets used by researchers, but those datasets are either outdated or incompatible with the SDN environment. In this survey, an overview of the published work was conducted using the InSDN dataset from 2020 to 2023. Also, research challenges and future work for further research on IDS issues when deployed in an SDN environment are discussed, particularly when employing machine learning and deep learning models. Moreover, possible solutions for each issue are provided to help the researchers carry out and develop new methods of secure SDN. https://etasr.com/index.php/ETASR/article/view/6756software defined networkingInSDNintrusion detection systemsnetwork securitydatasets |
| spellingShingle | Harman Yousif Ibrahim Khalid Najla Badie Ibrahim Aldabagh A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments Engineering, Technology & Applied Science Research software defined networking InSDN intrusion detection systems network security datasets |
| title | A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments |
| title_full | A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments |
| title_fullStr | A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments |
| title_full_unstemmed | A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments |
| title_short | A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments |
| title_sort | survey on the latest intrusion detection datasets for software defined networking environments |
| topic | software defined networking InSDN intrusion detection systems network security datasets |
| url | https://etasr.com/index.php/ETASR/article/view/6756 |
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