A Systematic Review of Various Deep Learning Techniques for Network Intrusion Detection System

Author:

Sasikumar A. N.ORCID,Lilly Sheeba S.ORCID

Publisher

Springer Nature Switzerland

Reference30 articles.

1. Kim, G., Yi, H., Lee, J., Paek, Y., Yoon, S.: LSTM-based system-call language modeling and robust ensemble method for designing host-based intrusion detection systems. arXiv preprint arXiv:1611.01726 (2016)

2. Park, D., Kim, S., Kwon, H., Shin, D., Shin, D.: Host-based intrusion detection model using Siamese network. IEEE Access 9, 76614–76623 (2021)

3. Kim, K.: GAN based augmentation for improving anomaly detection accuracy in host-based intrusion detection systems. Int. J. Eng. Res. Technol. 13, 3987 (2020)

4. Ouarda, L., Malika, B., Brahim, B.: Towards a better similarity algorithm for host-based intrusion detection system. J. Intell. Syst. 32(1), 20220259 (2023)

5. Rincy, N.T., Gupta, R.: Design and development of an efficient network intrusion detection system using machine learning techniques. Wirel. Commun. Mob. Comput. 2021, 1–35 (2021)

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