Metaheuristic assisted hybrid deep classifiers for intrusion detection: a bigdata perspective
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11276-024-03815-0.pdf
Reference41 articles.
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3. Hachmi, F., Boujenfa, K., & Limam, M. (2019). Enhancing the accuracy of intrusion detection systems by reducing the rates of false positives and false negatives through multi-objective optimization. Journal of Network and Systems Management, 27, 93–120.
4. Hsu, C. M., Azhari, M. Z., Hsieh, H. Y., Prakosa, S. W., & Leu, J. S. (2021). Robust network intrusion detection scheme using long-short term memory based convolutional neural networks. Mobile Networks and Applications, 26, 1137–1144.
5. Li, W., Meng, W., & Au, M. H. (2020). Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments. Journal of Network and Computer Applications, 161, 102631.
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