Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/4234
Title: | Comparison of Various ML Approaches for Detection of DDoS Attacks in SDN |
Authors: | Kujur, Priyanka Patel, Sanjeev |
Keywords: | DDoS Attacks, SDN Supervised machine learning approach Precision Recall |
Issue Date: | Dec-2023 |
Citation: | IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN), Bangkok, Thailand, 22-23 December 2023 |
Abstract: | The 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. |
Description: | Copyright belongs to proceeding publisher |
URI: | http://hdl.handle.net/2080/4234 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2023_CICN_PKujur_Comparison.pdf | 229.62 kB | Adobe PDF | View/Open Request a copy |
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