Multi-class intrusion detection system in SDN based on hybrid BiLSTM model
Author:
Funder
Science and Technology Commission of Shanghai Municipality
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10586-024-04477-5.pdf
Reference40 articles.
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3. Jazaeri, S.S., Jabbehdari, S., Asghari, P., Haj, S.J.H.: Edge computing in SDN-IoT networks: a systematic review of issues, challenges and solutions. Clust. Comput. 24, 3187–3228 (2021). https://doi.org/10.1007/s10586-021-03311-6
4. Pajouh, H.H., Javidan, R., Khayami, R., Dehghantanha, A., Choo, K.-K.R.: A Two-Layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerg. Topics Comput. 7, 314–323 (2019). https://doi.org/10.1109/TETC.2016.2633228
5. Gumus, F., Sakar, C.O., Erdem, Z., Kursun, O.: Online Naive Bayes classification for network intrusion detection. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014). pp. 670–674. IEEE, China (2014)
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