Network Intrusion Detection via Oversampling and Transformer-BiLSTM-MLP
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3332-3_3
Reference18 articles.
1. Benddi, H., Ibrahimi, K., Bensimane, A.: Improving the intrusion detection system for NSL-KDD dataset based on PCA-fuzzy clustering-KNN. In: 2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1–6 (2018). https://doi.org/10.1109/WINCOM.2018.8629718
2. Bhosale, K.S., Nenova, M., Iliev, G.: Modified Naive Bayes intrusion detection system (MNBIDS). In: 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), pp. 291–296 (2018). https://doi.org/10.1109/CTEMS.2018.8769248
3. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002). https://doi.org/10.1613/jair.953
4. Chen, W., Wang, H., Fei, M., Du, D., Rakić, A.: An intrusion detection method using Adasyn and Bayesian optimized lightgbm. In: 2022 34th Chinese Control and Decision Conference (CCDC), pp. 4622–4627 (2022). https://doi.org/10.1109/CCDC55256.2022.10033879
5. He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence, pp. 1322–1328 (2008). https://doi.org/10.1109/IJCNN.2008.4633969
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