DDoS Attack Detection Based on Information Entropy Feature Extraction in Software Defined Networks
2023 International Conference on Networking and Network Applications (NaNA)(2023)
摘要
Distributed Denial of Service(DDoS) attacks target the forwarding-control separation feature of Software-Defined Networking(SDN) to launch attacks, causing network disruptions. Therefore security against DDoS attack detection for SDN controllers is the focus of current research. This paper proposes an Extreme Gradient Boosting (XGBoost) DDoS attack detection algorithm based on a combination of information gain and recursive feature elimination algorithms. We evaluated the performance of the method using the CICDDoS2019 dataset. This method works well in multi-classification model for attack detection with an accuracy of 93.41%.
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关键词
software defined networking,distributed denial of service attack detection,information gain,recursive feature elimination,extreme gradient boosting algorithm
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