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ScalaDetect-5G: Ultra High-Precision Highly Elastic Deep Intrusion Detection System for 5G Network

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Publicat a:Computer Modeling in Engineering & Sciences vol. 144, no. 3 (2025), p. 3805-3828
Autor principal: Chang, Shengjia
Altres autors: Cui, Baojiang, Feng, Shaocong
Publicat:
Tech Science Press
Matèries:
Accuracy
Network function virtualization
Classification
Real time
5G mobile communication
Communication networks
Communications traffic
Representations
Edge computing
Intrusion detection systems
Mobile computing
Deep learning
Network topologies
Network security
Communication
Industrial Internet of Things
Communications networks
Spectrum allocation
Efficiency
Accés en línia:Citation/Abstract
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Resum:With the rapid advancement of mobile communication networks, key technologies such as Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) have enhanced the quality of service for 5G users but have also significantly increased the complexity of network threats. Traditional static defense mechanisms are inadequate for addressing the dynamic and heterogeneous nature of modern attack vectors. To overcome these challenges, this paper presents a novel algorithmic framework, SD-5G, designed for high-precision intrusion detection in 5G environments. SD-5G adopts a three-stage architecture comprising traffic feature extraction, elastic representation, and adaptive classification. Specifically, an enhanced Concrete Autoencoder (CAE) is employed to reconstruct and compress high-dimensional network traffic features, producing compact and expressive representations suitable for large-scale 5G deployments. To further improve accuracy in ambiguous traffic classification, a Residual Convolutional Long Short-Term Memory model with an attention mechanism (ResCLA) is introduced, enabling multi-level modeling of spatial–temporal dependencies and effective detection of subtle anomalies. Extensive experiments on benchmark datasets—including 5G-NIDD, CIC-IDS2017, ToN-IoT, and BoT-IoT—demonstrate that SD-5G consistently achieves F1 scores exceeding 99.19% across diverse network environments, indicating strong generalization and real-time deployment capabilities. Overall, SD-5G achieves a balance between detection accuracy and deployment efficiency, offering a scalable, flexible, and effective solution for intrusion detection in 5G and next-generation networks.
ISSN:1526-1492
1526-1506
DOI:10.32604/cmes.2025.067756
Font:Advanced Technologies & Aerospace Database