Intrusion Classification and Detection System Using Machine Learning Models on NSL-KDD Dataset
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9707-7_8
Reference27 articles.
1. Vishwakarma M, Kesswani N (2023) A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelop method for anomaly detection. Decis Anal J 7:100233
2. Kasongo SM (2023) A deep learning technique for intrusion detection system using a recurrent neural networks based framework. Comput Commun 199:113–125
3. Logeswari G, Bose S, Anitha T (2023) An intrusion detection system for SDN using machine learning. Intell Autom Soft Comput 35(1):867–880
4. Thakkar A, Lohiya R (2023) Fusion of statistical importance for feature selection in deep neural network-based intrusion detection system. Inf Fusion 90:353–363
5. Fuat TÜRK (2023) Analysis of intrusion detection systems in UNSW-NB15 and NSL-KDD datasets with machine learning algorithms. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12(2):465–477
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