Toward Secure 5G/6G Networks: Comparing Multimodal Fusion Methods for Attack Detection

The rapid development of 5G and emerging 6G networks introduces advanced security threats (e.g., DDoS, eavesdropping, jamming, etc.), necessitating advanced intrusion detection systems. Current studies use outdated benchmark datasets that do not capture the dynamic, AI-driven traffic of these networks. Additionally, they often rely on payload data for intrusion detection; however, in 5G/6G networks, payloads are typically encrypted, limiting the applicability and effectiveness of these approaches. While some research explores fusion methods, they typically overlook crucial inter- and intra-modal interactions. To address these limitations, this letter applies multimodal fusion methods on a publicly available dataset reflecting realistic 5G/6G conditions. Features are grouped into packet-related, time-related, and length-related modalities. Fusion methods, including bilinear pooling, gated multimodal unit, and block-term tensor decomposition, are employed. Results show that block term tensor decomposition achieves a weighted F1-score of 99.08%, outperforming the other fusion methods by 8.08-16.74%. This letter also provides a comparative discussion on the computational complexity of each fusion method. Finally, a series of ablation experiments is conducted to prove the effectiveness of the introduced approach.