Securing Industrial IoT with Cross-System Federated Learning for Malware Detection
Caihong Wang, Xu Du · 2025
The rapid advancement of Industrial Internet of Things (IIoT) technologies has exacerbated the threat of crosssystem malware in industrial systems. Conventional malware detection approaches, typically constrained to single-system environments, struggle to cope with malware mutations and crosssystem propagation. We propose a federated learning-driven cross-system malware detection framework FedCrossMal. This framework partitions heterogeneous software from different systems into shared and system-specific features, enabling collaborative learning of malware behaviors across systems. FedCrossMal empirical evaluations on real-world software samples spanning multiple systems demonstrate that the proposed framework significantly enhances cross-system malware detection while preserving the original system's high detection performance. This work provides a robust solution for mitigating cross-system malware threats in IIoT environments and offers foundational insights for future research.