Towards an efficient model for network intrusion detection system (IDS): systematic literature review
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s11276-023-03495-2.pdf
Reference143 articles.
1. Ashiku, L., & Dagli, C. (2021). Network intrusion detection system using deep learning. Procedia Computer Science, 185, 239–247. https://doi.org/10.1016/j.procs.2021.05.025
2. Gupta, N., Jindal, V., & Bedi, P. (2021). LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system. Computer Networks, 192, 108076. https://doi.org/10.1016/j.comnet.2021.108076
3. Nguyen, M. T., & Kim, K. (2020). Genetic convolutional neural network for intrusion detection systems. Future Generation Computer Systems, 113, 418–427. https://doi.org/10.1016/j.future.2020.07.042
4. Wu, Z., Wang, J., Hu, L., Zhang, Z., & Wu, H. (2020). A network intrusion detection method based on semantic re-encoding and deep learning. Journal of Network and Computer Applications, 164, 102688. https://doi.org/10.1016/j.jnca.2020.102688
5. Kim, J., Kim, J., Kim, H., Shim, M., & Choi, E. (2020). CNN-based network intrusion detection against denial-of-service attacks. Electronics. https://doi.org/10.3390/electronics9060916
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