A Survey on DDoS Detection Using Deep Learning in Software Defined Networking
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3481-2_37
Reference17 articles.
1. Ahuja N, Singal G, Mukhopadhyay D (2021) DLSDN: deep learning for DDOS attack detection in software defined networking. In: 2021 11th international conference on cloud computing, data science & engineering (confluence). IEEE, pp 683–688
2. Elsayed MS, Le-Khac NA, Dev S, Jurcut AD (2020) Ddosnet: a deep-learning model for detecting network attacks. In: 2020 IEEE 21st international symposium on “A world of wireless, mobile and multimedia networks” (WoWMoM). IEEE, pp 391–396
3. Haider S, Akhunzada A, Mustafa I, Patel TB, Fernandez A, Choo KKR, Iqbal J (2020) A deep CNN ensemble framework for efficient DDOS attack detection in software defined networks. Ieee Access 8:53972–53983
4. Lara A, Kolasani A, Ramamurthy B (2013) Network innovation using openflow: a survey. IEEE Commun Surv Tutor 16(1):493–512
5. Lee TH, Chang LH, Syu CW (2020) Deep learning enabled intrusion detection and prevention system over SDN networks. In: 2020 IEEE international conference on communications workshops (ICC workshops). IEEE, pp 1–6
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