Column-Wise Autoencoder Representation Learning for Intrusion Detection in Multi-MEC Edge Networks

Mobile Edge Computing (MEC) is a key enabler of 5G/6G services, but multi-base-station deployment enlarges the attack surface and motivates edge-native intrusion detection systems (IDSs). Existing MEC-based IDSs are mainly single-node or centralized, which struggle with heterogeneous traffic across next-generation Node Bs (gNBs) and incur latency and network load due to data aggregation. To address these limitations, this paper proposes a Column-Wise Autoencoder Ensemble (CW-AE) distributed learning framework for multi-MEC environments. Each MEC node trains column-wise autoencoder encoders locally to extract compact latent features, and a master MEC trains a stacking-based meta-classifier using concatenated latent features, avoiding raw traffic transfer and parameter averaging. By preserving node-specific behavior while integrating heterogeneous features, CW-AE improves detection performance and reduces communication overhead. Using the real-world 5G-NIDD dataset collected from two physical 5G base stations, we compare local single-node, centralized, and CW-AE-based distributed learning. The results show that CW-AE achieves superior detection capability and network efficiency, making it suitable for scalable edge IDS deployments.