Network Intrusion Detection with Incremental Active Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57942-4_33
Reference11 articles.
1. Amalapuram, S., Tadwai, A., Vinta, R., Channappayya, S., Tamma, B.: Continual learning for anomaly based network intrusion detection. In: 2022 14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022, pp. 497–505 (2022). https://doi.org/10.1109/COMSNETS53615.2022.9668482
2. Boukela, L., Zhang, G., Yacoub, M., Bouzefrane, S.: A near-autonomous and incremental intrusion detection system through active learning of known and unknown attacks. In: Conference Digest - 2021 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2021, pp. 374–379 (2021). https://doi.org/10.1109/SPAC53836.2021.9539947
3. Gamage, S., Samarabandu, J.: Deep learning methods in network intrusion detection: a survey and an objective comparison. J. Network Comput. Appl. 169, 102767 (2020). https://doi.org/10.1016/j.jnca.2020.102767
4. Li, J., Wu, W., Xue, D.: An intrusion detection method based on active transfer learning. Intell. Data Anal. 24, 263–283 (2020). https://doi.org/10.3233/IDA-194487
5. Lin, J., Wei, Y., Li, W., Long, J.: Intrusion detection system based on deep neural network and incremental learning for in-vehicle CAN networks. In: Wang, G., Choo, KK.R., Ko, R.K.L., Xu, Y., Crispo, B. (eds.) Ubiquitous Security. UbiSec 2021. Communications in Computer and Information Science, vol. 1557 CCIS. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-0468-4_19
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