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.
Authors
- Nam‐Phong Nguyen
- Huy‐Trung Nguyen (ORCID: https://orcid.org/0000-0002-2710-5326)
- T. D. Nguyen (ORCID: https://orcid.org/0000-0002-3862-5853)
- Mai Thi Nguyen (ORCID: https://orcid.org/0000-0002-6469-9514)
- Hai-Viet Le (ORCID: https://orcid.org/0000-0002-2623-2462)
Institutions
- 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)
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