One-Dimensional Convolutional Neural Network for Detection and Mitigation of DDoS Attacks in SDN
In Software-Defined Networking (SDN), the controller plane is an essential component in managing network traffic because of its global knowledge of the network and its management applications. However, an attacker might attempt to direct malicious traffic towards the controller, paralyzing the entir...
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| Veröffentlicht in: | MLN (4. : 2021 : Online) Machine learning for networking |
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| Format: | UnknownFormat |
| Sprache: | eng |
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2022
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Tag hinzufügen | Zusammenfassung: | In Software-Defined Networking (SDN), the controller plane is an essential component in managing network traffic because of its global knowledge of the network and its management applications. However, an attacker might attempt to direct malicious traffic towards the controller, paralyzing the entire network. In this work, a One-Dimensional Convolutional Neural Network (1D-CNN) is used to protect the controller evaluating entropy information. Therefore, the CICDDoS2019 dataset is used to investigate the proposed approach to train and evaluate the performance of the model and then examine the effectiveness of the proposal in the SDN environment. The experimental results manifest that the proposed approach achieves very high enhancements in terms of accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) for the detection of Distributed Denial of Service (DDoS) attacks compared to one of the benchmarking state of the art approaches. |
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| ISBN: | 9783030989774 |