A Novel Network Attack Detection Platform Targeting the <scp>AMF</scp> Component in the <scp>5G</scp> Network Infrastructure

ABSTRACT In the trend of the Internet of Things, 5G technology is one of the important platforms connecting mobile devices to the Internet network. Along with the popularity of 5G network deployment in many countries, the risk of destructive attacks on this infrastructure is increasing. This paper proposes a novel platform for detecting attacks on the AMF in 5G cores using distributed ML. Core innovation: The Attack‐Aware Weighted Aggregation (AAWA) in federated learning enables knowledge sharing without raw data exchange, achieving 99.24% global accuracy (0.77% over FedAvg), 25.6% faster convergence (32 vs. 43 rounds), and robust privacy with differential privacy ( ε = 1.0, 0.33% accuracy drop). Experiments on a custom AMF dataset validate superior performance in detection, efficiency, and resilience.

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Publication Details

Journal
Concurrency and Computation Practice and Experience
Published
2025-12-12
DOI
https://doi.org/10.1002/cpe.70452
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

A Novel Network Attack Detection Platform Targeting the AMF Component in the 5G Network Infrastructure

Nam‐Phong Nguyen, Huy‐Trung Nguyen, T. D. Nguyen, Mai Thi Nguyen et al.
Concurrency and Computation Practice and Experience
Network Security and Intrusion Detection
article

A Novel Network Attack Detection Platform Targeting the AMF Component in the 5G Network Infrastructure

Nam‐Phong Nguyen, Huy‐Trung Nguyen, T. D. Nguyen, Mai Thi Nguyen, Hai-Viet Le
article en

Abstract

ABSTRACT In the trend of the Internet of Things, 5G technology is one of the important platforms connecting mobile devices to the Internet network. Along with the popularity of 5G network deployment in many countries, the risk of destructive attacks on this infrastructure is increasing. This paper proposes a novel platform for detecting attacks on the AMF in 5G cores using distributed ML. Core innovation: The Attack‐Aware Weighted Aggregation (AAWA) in federated learning enables knowledge sharing without raw data exchange, achieving 99.24% global accuracy (0.77% over FedAvg), 25.6% faster convergence (32 vs. 43 rounds), and robust privacy with differential privacy ( ε = 1.0, 0.33% accuracy drop). Experiments on a custom AMF dataset validate superior performance in detection, efficiency, and resilience.

Concurrency and Computation Practice and ExperienceVol. 38(1)
Vietnam Posts and Telecommunications Group (Vietnam) (VN), Diplomatic Academy of Vietnam (VN), Research Institute of Posts and Telecommunications (SK), Vietnam Academy of Science and Technology (VN)
Openalex Percentile: Top 55%
Network Security and Intrusion Detection
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A Novel Network Attack Detection Platform Targeting the <scp>AMF</scp> Component in the <scp>5G</scp> Network Infrastructure — Nam‐Phong Nguyen, Huy‐Trung Nguyen, et al. · Concurrency and Computation Practice and Experience (2025) | TGRS Research Map | TGRS