Intrusion detection systems for software-defined networks: a comprehensive study on machine learning-based techniques-Reference-Cited by-同舟云学术

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Intrusion detection systems for software-defined networks: a comprehensive study on machine learning-based techniques

Published:2024-04-25 Issue:7 Volume:27 Page:9635-9661
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Mustafa Zaid,Amin Rashid,Aldabbas Hamza,Ahmed Naeem

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10586-024-04430-6.pdf

Reference92 articles.

1. Abdou, A., Oorschot, P.C., Wan, T.: Comparative analysis of control plane security of SDN and conventional networks. IEEE Commun. Surv. Tutor. 20(4), 3542–3559 (2018). https://doi.org/10.1109/COMST.2018.2839348

2. Elsayed, R.A., Hamada, R.A., Abdalla, M.I., Elsaid, S.A.: Securing IoT and SDN systems using deep-learning based automatic intrusion detection. Ain Shams Eng. J. 14(10), 102211 (2023)

3. Bera, S., Misra, S., Vasilakos, A.V.: Software-defined networking for internet of things: a survey. IEEE Internet Things J. 4(6), 1994–2008 (2017). https://doi.org/10.1109/JIOT.2017.2746186

4. Amin, R., Hussain, M., Bibi, S., Sabir, A.: Policy-based data analytic for software defined wireless sensor networks. In: Afzal, M.K., Ateeq, M., Kim, S.W. (eds.) Data-Driven Intelligence in Wireless Networks, pp. 189–212. CRC Press, Boca Raton (2023)

5. Huang, C.-H., Lee, T.-H., Chang, L.-H., Lin, J.-R., Horng, G.: Adversarial attacks on SDN-based deep learning ids system. (2018)

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1. Improving Intrusion Detection in a Software Defined Network using Hybrid CNN and Bi-LSTM;2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2024-06-05

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