Abstract
SDN is a new network architecture with centralized control. By analyzing the traffic characteristics of DDoS attack, and using the SDN controller to collect the traffic in the network, the important characteristics such as the IP address entropy ratio and the port entropy ratio related to the attack are extracted. According to the analysis of relevant eigenvalues, the RBFNN algorithm is used to classify the training samples to detect DDoS attacks. Finally, the SDN environment and DDoS attacks are simulated under Ubuntu, and the RBFNN algorithm detection model is deployed in the SDN controller. Compared with BPNN algorithm and Naive Bayes algorithm, it is proved that the algorithm performs DDoS attack detection with high recognition rate in a short time.
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Acknowledgement
This work was supported by National Key Research and Development Plan of China (No 2016YFB0801004).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, J., Zhang, M., Wang, J. (2019). DDoS Attack Detection Based on RBFNN in SDN. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_23
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DOI: https://doi.org/10.1007/978-3-030-19086-6_23
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