Ensemble Learning Based Big Data Classification for Intrusion Detection
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-27440-4_48
Reference26 articles.
1. Bagui, S., Li, K.: Resampling imbalanced data for network intrusion detection datasets. J. Big Data 8(1), 1–41 (2021). https://doi.org/10.1186/s40537-020-00390-x
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3. Chand, N., Mishra, P., Krishna, C.R., Pilli, E.S., Govil, M.C.: A comparative analysis of svm and its stacking with other classification algorithm for intrusion detection. In: 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Spring), pp. 1–6. IEEE (2016)
4. Chitrakar, R., Huang, C.: Anomaly based intrusion detection using hybrid learning approach of combining k-medoids clustering and Naive Bayes classification. In: 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–5. IEEE (2012)
5. Chowdhury, R., Sen, S., Roy, A., Saha, B.: An optimal feature based network intrusion detection system using bagging ensemble method for real-time traffic analysis. Multimedia Tools and Applications, pp. 1–23 (2022)
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