Self-Supervised Learning for Network Traffic Analysis in 5G Environments

The extensive deployment of$\mathbf{5 G}$networks facilitates more complicated issues on traffic identification, like high-speed data transmission, dynamic and diverse traffic patterns, and evolving security threats. Traditional supervised learning solutions suffer from a paucity of labeled data to input and the necessity for real-time adjustability. However, selfsupervised learning (SSL) has emerged as a beneficial alternative that utilizes unlabeled traffic data to generate strong representations that do not require manual annotations. This paper examines the SSL approaches for$\mathbf{5 G}$traffic analysis that cover anomaly detection, traffic classification, and intrusion prevention. These are then used for pre-training models on large-scale unlabeled traffic datasets with contrastive learning or reconstruction-based objectives and enable the approach to acquire valuable features that could improve downstream tasks. Their main novelties comprise adaptive pretext tasks optimized for 5 G peculiarities (e.g., slicing-aware embeddings), and lightweight edge-friendly architectures. Results on popular realworld 5G datasets show that SSL increases detection accuracy by$\mathbf{1 5 - 2 0 \%}$over supervised baselines in low-labeling scenarios, achieving sub-millisecond latency for real-time processing. It is also robust to new attack vectors and network conditions (high generalization). This research enhances computational efficiency for large-scale deployments by optimizing models for 5G edge nodes. The experiments further indicate that delays in secure streaming can significantly disrupt the functioning of autonomous network managers in 5 G, highlighting the importance of scalability, adaptability, and efficiency in nextgeneration traffic analysis.

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