DDoS Detection Approach Based on Continual Learning in the SDN Environment
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-27409-1_110
Reference20 articles.
1. Kreutz, D., Ramos, F.M., Verissimo, P.E., Rothenberg, C.E., Azodolmolky, S., Uhlig, S.: Software-defined networking: a comprehensive survey. Proc. IEEE 103(1), 14–76 (2014)
2. Chetouane, A., Karoui, K.: A survey of machine learning methods for DDoS threats detection against SDN. In: International Workshop on Distributed Computing for Emerging Smart Networks, pp. 99–127. Springer (2022)
3. Kreutz, D., Ramos, F.M.V., Verissimo, P.: Towards secure and dependable software-defined networks. In: Proceedings of the Second ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking, pp. 55–60 (2013)
4. Sachdeva, M., Singh, G., Kumar, K., Singh, K.: Measuring impact of DDoS attacks on web services (2010)
5. Liao, H.J., Lin, C.H.R., Lin, Y.C., Tung, K.Y.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16–24 (2013)
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