A Simple Distributed Approach for Running Machine Learning Based Simulations in Intrusion Detection Systems
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-51572-9_6
Reference19 articles.
1. Dask. https://www.dask.org/
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3. Fernandes, R., Silva, J., Ribeiro, O., Portela, I., Lopes, N.: The impact of identifiable features in ML classification algorithms with the HIKARI-2021 dataset. In: 2023 11th International Symposium on Digital Forensics and Security (ISDFS), pp. 1–5 (2023). https://doi.org/10.1109/ISDFS58141.2023.10131864
4. Ferriyan, A., Thamrin, A.H., Takeda, K., Murai, J.: Generating network intrusion detection dataset based on real and encrypted synthetic attack traffic. Appl. Sci. 11(17), 7868 (2021). https://doi.org/10.3390/app11177868
5. Jamalipour, A., Murali, S.: A taxonomy of machine-learning-based intrusion detection systems for the internet of things: a survey. IEEE Internet Things J. 9(12), 9444–9466 (2022). https://doi.org/10.1109/JIOT.2021.3126811
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