Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks

Vincent Andrew Akpan; Emmanuel Chinenye Njoku; Ebubechukwu Ifeoluwa Obi

doi:doi:10.11648/j.wcmc.20251202.14

Research Article |

| Peer-Reviewed

Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks

Vincent Andrew Akpan^*

, Emmanuel Chinenye Njoku

, Ebubechukwu Ifeoluwa Obi

Published in International Journal of Wireless Communications and Mobile Computing (Volume 12, Issue 2)

Received: 26 September 2025 Accepted: 9 October 2025 Published: 26 November 2025

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Abstract

The security challenges introduced by 5G network slicing demand tailored intrusion detection systems (IDS). Traditional intrusion detection systems (IDS) and intrusion detection and prevention systems (IDPS) frameworks, built for static network configurations, are inadequate for the dynamic and heterogeneous nature of 5G networks. To address this gap, this study develops and evaluates slice-specific machine learning models to enhance intrusion detection across different 5G slices, namely: enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC), and Ultra-Reliable Low-Latency Communication (URLLC). Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) models were applied to publicly available datasets representing each slice. These models are assessed based on their accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), confusion matrix and execution time. The results reveal that the LSTM model achieved the highest accuracy and AUC-ROC scores for the eMBB and mMTC slices, making it suitable for applications where detection accuracy is critical despite higher computational demands. In contrast, Random Forest demonstrated superior computational efficiency, making it the most preferred model for latency-sensitive URLLC slice, where real-time detection is essential. While the SVM model performed well in terms of accuracy, its high computational cost renders it less practical for real-time applications, particularly in URLLC environments. This research provides insights for enhancing 5G network security through the deployment of slice-specific machine learning models, thereby addressing the critical need for adaptable and efficient IDS frameworks.

Published in	International Journal of Wireless Communications and Mobile Computing (Volume 12, Issue 2)
DOI	10.11648/j.wcmc.20251202.14
Page(s)	93-118
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Fifth Generation (5G) Networks, Intrusion Detection System (IDS), Machine Learning Algorithms, Massive Machine-Type Communication (mMTC), Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC)

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Akpan, V. A., Njoku, E. C., Obi, E. I. (2025). Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks. International Journal of Wireless Communications and Mobile Computing, 12(2), 93-118. https://doi.org/10.11648/j.wcmc.20251202.14

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Akpan, V. A.; Njoku, E. C.; Obi, E. I. Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks. Int. J. Wirel. Commun. Mobile Comput. 2025, 12(2), 93-118. doi: 10.11648/j.wcmc.20251202.14

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Akpan VA, Njoku EC, Obi EI. Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks. Int J Wirel Commun Mobile Comput. 2025;12(2):93-118. doi: 10.11648/j.wcmc.20251202.14

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@article{10.11648/j.wcmc.20251202.14,
  author = {Vincent Andrew Akpan and Emmanuel Chinenye Njoku and Ebubechukwu Ifeoluwa Obi},
  title = {Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks
},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {12},
  number = {2},
  pages = {93-118},
  doi = {10.11648/j.wcmc.20251202.14},
  url = {https://doi.org/10.11648/j.wcmc.20251202.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20251202.14},
  abstract = {The security challenges introduced by 5G network slicing demand tailored intrusion detection systems (IDS). Traditional intrusion detection systems (IDS) and intrusion detection and prevention systems (IDPS) frameworks, built for static network configurations, are inadequate for the dynamic and heterogeneous nature of 5G networks. To address this gap, this study develops and evaluates slice-specific machine learning models to enhance intrusion detection across different 5G slices, namely: enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC), and Ultra-Reliable Low-Latency Communication (URLLC). Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) models were applied to publicly available datasets representing each slice. These models are assessed based on their accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), confusion matrix and execution time. The results reveal that the LSTM model achieved the highest accuracy and AUC-ROC scores for the eMBB and mMTC slices, making it suitable for applications where detection accuracy is critical despite higher computational demands. In contrast, Random Forest demonstrated superior computational efficiency, making it the most preferred model for latency-sensitive URLLC slice, where real-time detection is essential. While the SVM model performed well in terms of accuracy, its high computational cost renders it less practical for real-time applications, particularly in URLLC environments. This research provides insights for enhancing 5G network security through the deployment of slice-specific machine learning models, thereby addressing the critical need for adaptable and efficient IDS frameworks.
},
 year = {2025}
}

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TY  - JOUR
T1  - Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks

AU  - Vincent Andrew Akpan
AU  - Emmanuel Chinenye Njoku
AU  - Ebubechukwu Ifeoluwa Obi
Y1  - 2025/11/26
PY  - 2025
N1  - https://doi.org/10.11648/j.wcmc.20251202.14
DO  - 10.11648/j.wcmc.20251202.14
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 93
EP  - 118
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20251202.14
AB  - The security challenges introduced by 5G network slicing demand tailored intrusion detection systems (IDS). Traditional intrusion detection systems (IDS) and intrusion detection and prevention systems (IDPS) frameworks, built for static network configurations, are inadequate for the dynamic and heterogeneous nature of 5G networks. To address this gap, this study develops and evaluates slice-specific machine learning models to enhance intrusion detection across different 5G slices, namely: enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC), and Ultra-Reliable Low-Latency Communication (URLLC). Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) models were applied to publicly available datasets representing each slice. These models are assessed based on their accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), confusion matrix and execution time. The results reveal that the LSTM model achieved the highest accuracy and AUC-ROC scores for the eMBB and mMTC slices, making it suitable for applications where detection accuracy is critical despite higher computational demands. In contrast, Random Forest demonstrated superior computational efficiency, making it the most preferred model for latency-sensitive URLLC slice, where real-time detection is essential. While the SVM model performed well in terms of accuracy, its high computational cost renders it less practical for real-time applications, particularly in URLLC environments. This research provides insights for enhancing 5G network security through the deployment of slice-specific machine learning models, thereby addressing the critical need for adaptable and efficient IDS frameworks.

VL  - 12
IS  - 2
ER  -

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Author Information

Vincent Andrew Akpan

Department of Biomedical Engineering, The Federal University of Technology, Akure, Nigeria

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http://orcid.org/0000-0002-5155-2863
Emmanuel Chinenye Njoku

ICT Department, Guaranty Trust Holding Company, Victoria Island, Nigeria

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http://orcid.org/0009-0007-8460-9823
Ebubechukwu Ifeoluwa Obi

Human Resources Unit, Human Family Foundation for Peace and Development, Osogbo, Nigeria

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http://orcid.org/0009-0009-7820-9959

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APA Style

Akpan, V. A., Njoku, E. C., Obi, E. I. (2025). Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks. International Journal of Wireless Communications and Mobile Computing, 12(2), 93-118. https://doi.org/10.11648/j.wcmc.20251202.14

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ACS Style

Akpan, V. A.; Njoku, E. C.; Obi, E. I. Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks. Int. J. Wirel. Commun. Mobile Comput. 2025, 12(2), 93-118. doi: 10.11648/j.wcmc.20251202.14

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AMA Style

Akpan VA, Njoku EC, Obi EI. Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks. Int J Wirel Commun Mobile Comput. 2025;12(2):93-118. doi: 10.11648/j.wcmc.20251202.14

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@article{10.11648/j.wcmc.20251202.14,
  author = {Vincent Andrew Akpan and Emmanuel Chinenye Njoku and Ebubechukwu Ifeoluwa Obi},
  title = {Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks
},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {12},
  number = {2},
  pages = {93-118},
  doi = {10.11648/j.wcmc.20251202.14},
  url = {https://doi.org/10.11648/j.wcmc.20251202.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20251202.14},
  abstract = {The security challenges introduced by 5G network slicing demand tailored intrusion detection systems (IDS). Traditional intrusion detection systems (IDS) and intrusion detection and prevention systems (IDPS) frameworks, built for static network configurations, are inadequate for the dynamic and heterogeneous nature of 5G networks. To address this gap, this study develops and evaluates slice-specific machine learning models to enhance intrusion detection across different 5G slices, namely: enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC), and Ultra-Reliable Low-Latency Communication (URLLC). Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) models were applied to publicly available datasets representing each slice. These models are assessed based on their accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), confusion matrix and execution time. The results reveal that the LSTM model achieved the highest accuracy and AUC-ROC scores for the eMBB and mMTC slices, making it suitable for applications where detection accuracy is critical despite higher computational demands. In contrast, Random Forest demonstrated superior computational efficiency, making it the most preferred model for latency-sensitive URLLC slice, where real-time detection is essential. While the SVM model performed well in terms of accuracy, its high computational cost renders it less practical for real-time applications, particularly in URLLC environments. This research provides insights for enhancing 5G network security through the deployment of slice-specific machine learning models, thereby addressing the critical need for adaptable and efficient IDS frameworks.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Slice-Specific Machine Learning Models for Intrusion Detection in 5G Telecommunication Networks

AU  - Vincent Andrew Akpan
AU  - Emmanuel Chinenye Njoku
AU  - Ebubechukwu Ifeoluwa Obi
Y1  - 2025/11/26
PY  - 2025
N1  - https://doi.org/10.11648/j.wcmc.20251202.14
DO  - 10.11648/j.wcmc.20251202.14
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 93
EP  - 118
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20251202.14
AB  - The security challenges introduced by 5G network slicing demand tailored intrusion detection systems (IDS). Traditional intrusion detection systems (IDS) and intrusion detection and prevention systems (IDPS) frameworks, built for static network configurations, are inadequate for the dynamic and heterogeneous nature of 5G networks. To address this gap, this study develops and evaluates slice-specific machine learning models to enhance intrusion detection across different 5G slices, namely: enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC), and Ultra-Reliable Low-Latency Communication (URLLC). Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) models were applied to publicly available datasets representing each slice. These models are assessed based on their accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), confusion matrix and execution time. The results reveal that the LSTM model achieved the highest accuracy and AUC-ROC scores for the eMBB and mMTC slices, making it suitable for applications where detection accuracy is critical despite higher computational demands. In contrast, Random Forest demonstrated superior computational efficiency, making it the most preferred model for latency-sensitive URLLC slice, where real-time detection is essential. While the SVM model performed well in terms of accuracy, its high computational cost renders it less practical for real-time applications, particularly in URLLC environments. This research provides insights for enhancing 5G network security through the deployment of slice-specific machine learning models, thereby addressing the critical need for adaptable and efficient IDS frameworks.

VL  - 12
IS  - 2
ER  -

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