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

Abstract

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.