Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4234
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dc.contributor.authorKujur, Priyanka-
dc.contributor.authorPatel, Sanjeev-
dc.date.accessioned2024-01-04T10:35:15Z-
dc.date.available2024-01-04T10:35:15Z-
dc.date.issued2023-12-
dc.identifier.citationIEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN), Bangkok, Thailand, 22-23 December 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4234-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThe network has become increasingly complicated and diversified in recent years due to the rapid growth of internet technology and its coverage. Software-defined networking (SDN) is a recent approach to network architecture. DDoS attacks have a measurable effect on SDN because of their centralized nature. This results in an early discovery, and prevention of DDoS attacks is essential. DDoS is a network risk that tries to flood targeted networks with unwanted data. This paper focuses on various ML approaches for classifying DDoS attacks by providing decision-making. That results in making the computing system more intelligent. We have used the most popular ML techniques to identify the attacks. To prevent and mitigate attacks, the evaluation process includes training and adoption of a suitable model for the specific network.en_US
dc.subjectDDoS Attacks,en_US
dc.subjectSDNen_US
dc.subjectSupervised machine learning approachen_US
dc.subjectPrecisionen_US
dc.subjectRecallen_US
dc.titleComparison of Various ML Approaches for Detection of DDoS Attacks in SDNen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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