Probability Boosted Regression for Intrusion Detection in Cyberactive Space
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47942-7_22
Reference16 articles.
1. Mauro, M.D., Galatro, G., Liotta, A.: Experimental review of neural-based approaches for network intrusion management. IEEE Trans. Netw. Serv. Manag. 17(4), 2480–2495 (2020). https://doi.org/10.1109/TNSM.2020.3024225
2. Pontes, C.F.T., de Souza, M.M.C., Gondim, J.J.C., Bishop, M., Marotta, M.A.: A new method for flow-based network intrusion detection using the inverse Potts model. IEEE Trans. Netw. Serv. Manag. 18(2), 1125–1136 (2021). https://doi.org/10.1109/TNSM.2021.3075503
3. Hu, B., et al.: A deep one-class intrusion detection scheme in software-defined industrial networks. IEEE Trans. Industr. Inform. 18(6), 4286–4296 (2022). https://doi.org/10.1109/TII.2021.3133300
4. Duan, G., Lv, H., Wang, H., Feng, G.: Application of a dynamic line graph neural network for intrusion detection with semisupervised learning. IEEE Trans. Inf. Forensics Secur. 18, 699–714 (2023). https://doi.org/10.1109/TIFS.2022.3228493
5. Gümüşbaş, D., Yıldırım, T., Genovese, A., Scotti, F.: A comprehensive survey of databases and deep learning methods for cybersecurity and intrusion detection systems. IEEE Syst. J. 15(2), 1717–1731 (2021). https://doi.org/10.1109/JSYST.2020.2992966
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