An ensemble approach-based intrusion detection system utilizing ISHO-HBA and SE-ResNet152
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Safety, Risk, Reliability and Quality,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10207-023-00777-w.pdf
Reference42 articles.
1. Zhou, X., Liang, W., Li, W., Yan, K., Shimizu, S., Kevin, I., Wang, K.: Hierarchical adversarial attacks against graph-neural-network-based IoT network intrusion detection system. IEEE Internet Things J. 9(12), 9310–9319 (2021)
2. Sarhan, M., Layeghy, S., Portmann, M.: Towards a standard feature set for network intrusion detection system datasets. In: Mobile Networks and Applications, pp. 1–14 (2022).
3. Alzahrani, A.O., Alenazi, M.J.: Designing a network intrusion detection system based on machine learning for software-defined networks. Future Internet 13(5), 111 (2021)
4. Choi, H., Kim, M., Lee, G., Kim, W.: Unsupervised learning approach for network intrusion detection system using autoencoders. J. Supercomput.Supercomput. 75, 5597–5621 (2019)
5. Ashiku, L., Dagli, C.: Network intrusion detection system using deep learning. Procedia Comput. Sci. 185, 239–247 (2021)
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