Hybrid Intrusion Detection System Using Autoencoders and Snort
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3758-5_36
Reference23 articles.
1. Yang L, Song Y, Gao S, Hu A, Xiao B (2022) Griffin: real-time network intrusion detection system via ensemble of autoencoder in SDN. IEEE Trans Netw Serv Manage 19(3):2269–2281. https://doi.org/10.1109/TNSM.2022.3175710
2. Deng H, Yang T (2021) Network intrusion detection based on sparse autoencoder and IGA-BP network. Wirel Commun Mob Comput, Article ID 9510858, 11p
3. Lu J, Meng H, Li W, Liu Y, Guo Y, Yang Y (2021) Network intrusion detection based on contractive sparse stacked denoising autoencoder. In: 2021 IEEE International symposium on broadband multimedia systems and broadcasting (BMSB), Chengdu, China, pp 1–6. https://doi.org/10.1109/BMSB53066.2021.9547087
4. Gharib M, Mohammadi B, Dastgerdi SH, Sabokrou M (2019) AutoIDS: auto-encoder based method for intrusion detection system. ArXiv abs/1911.03306
5. Mebawondu OJ, Popoọla OS, Ayogu II, Ugwu CC, Adetunmbi AO (2022) Network intrusion detection models based on Naives Bayes and C4.5 algorithms. In: 2022 IEEE Nigeria 4th international conference on disruptive technologies for sustainable development (NIGERCON), pp 1–5. https://doi.org/10.1109/NIGERCON54645.2022.9803086
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