Cross-network transferability of AI-based network intrusion detection systems in heterogeneous Internet of Things environments
Francesco Cerasuolo, Giampaolo Bovenzi, Antonio Pescapè · Computer Networks · 2026
The rapid expansion of Internet of Things (IoT) ecosystems has amplified the demand for robust cybersecurity solutions, with Artificial Intelligence (AI)-based Network Intrusion Detection System (NIDS) emerging as a promising component of modern defense strategies. Despite their strong performance when trained and evaluated on traffic collected within the same IoT environment, a critical open question remains: can these systems transfer effectively when deployed in different IoT networks? In this work, we present a comprehensive experimental study evaluating the cross-network transferability of AI-based NIDS built using Machine Learning (ML) and Deep Learning (DL), including attention-based architectures. Our analysis spans eight heterogeneous IoT network environments, represented by publicly available datasets. Specifically, we ( i ) assess attack-level transferability across networks, ( i i ) quantify the impact of feature informativeness on cross-network performance, ( i i i ) leverage eXplainable Artificial Intelligence (XAI) to interpret decisions in cross-network scenarios, ( i v ) investigate design principles for universal NIDS, and ( v ) evaluate edge-device deployment feasibility. Our findings provide systematic insight into the limits of AI-driven NIDS. Notably, cross-network transferability is highly variable and strongly influenced by attack semantics and dataset characteristics: volumetric attacks transfer effectively, whereas others remain dataset-dependent. Transferability benefits from generalizable feature design and multi-domain training, yet universal robustness remains challenging. Finally, while edge deployment is feasible from a memory perspective, Convolutional Neural Network (CNN) architectures offer substantially lower inference latency than Transformers on resource-constrained devices.