An empirical assessment of ML models for 5G network intrusion detection: A data leakage-free approach

Author:

Bouke Mohamed AlyORCID,Abdullah AzizolORCID

Publisher

Elsevier BV

Reference69 articles.

1. From intrusion detection to attacker attribution: a comprehensive survey of unsupervised methods;Nisioti;IEEE Commun. Surv. Tutorials,2018

2. Enhanced detection of imbalanced malicious network traffic with regularized Generative Adversarial Networks;Chapaneri;J. Netw. Comput. Appl.,2022

3. SMRD: a novel cyber warfare modeling framework for social engineering, malware, ransomware, and distributed denial-of-service based on a system of nonlinear differential equations;Bouke;J. Appl. Artif. Intell.,2024

4. Network based intrusion detection using the UNSW-NB15 dataset;Meftah;Int. J. Comput. Digit. Syst.,2019

5. The evaluation of Network Anomaly Detection Systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set;Moustafa;Inf. Secur. J.,2016

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