Lightweight Multi-Class Autoencoder Model for Malicious Traffic Detection in Private 5G Networks

This study proposes a lightweight autoencoder-based detection framework for the efficient detection of multi-class malicious traffic within a private 5G network slicing environment. Conventional deep learning-based detection approaches encounter difficulties in real-time processing and edge environment applications because of their significant computational complexity and resource demands. To address this issue, this study balances traffic data using slice-label-based hierarchical sampling and performs domain-specific feature grouping to reflect semantic similarity. Independent autoencoders are trained for each group, and the latent vectors from the encoder outputs are combined to be used as input for an SVM-based multi-class classifier. This structure reflects traffic differences between slices while also improving computational efficiency. Four sets of experiments were constructed to verify the model’s performance and evaluate its structural performance, resource usage efficiency, classifier generalization performance, and whether it met SLA constraints from various perspectives. As a result, the proposed Multi-AE model achieved an accuracy of 0.93, a balanced accuracy of 0.93, and an ECE of 0.03, demonstrating high stability and detection reliability. Regarding resource utilization efficiency, GPU utilization was under 7%, and the average memory usage was approximately 5.7 GB, demonstrating resource efficiency. In SLA verification, inference latency below 10 ms and a throughput of 564 samples/s were achieved based on URLLC. This study is significant in that it experimentally demonstrated a detection structure that achieves a balance of accuracy, lightweight design, and real-time performance in a 5G slicing environment.