Detection and Mitigation of DDOS Attack Using CART-SVC Approach in SDN
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3191-6_27
Reference11 articles.
1. Najar, A.A., Manohar Naik, S.:DDoS attack detection using MLP and random forest algorithms. Int. J. Inf. Tecnol. 14, 2317–2327 (2022)
2. Clerk Maxwell, J.: A Treatise on Electricity and Magnetism, 3rd edn, vol. 2, pp. 68–73. Clarendon, Oxford (1892)
3. Perez-Diaz, J.A., Valdovinos, I.A., Choo, K.-K.R., Zhu, D.: A flexible SDN-based architecture for ıdentifying and mitigating low-rate DDoS attacks using machine learning. IEEE Access 8, 155859–155872 (2020)
4. Yungaicela-Naula, N.M., Vargas-Rosales, C., Perez-Diaz, J.A.: SDN-based architecture for transport and application layer DDoS attack detection by using machine and deep learning. IEEE Access 9, 108495–108512 (2021)
5. Sangodoyin, A.O., Akinsol, M.O., Pillai, P., Grout, V.: Detection and classification of DDoS flooding attacks on software-defined networks: a case study for the application of machine learning. IEEE Access 9, 122495–122508 (2021)
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