Cross-domain collaboration under wireless communication scenarios has gained traction in Internet-of-Things (IoT) applications. Authentication is essential for ensuring the security of wireless IoT devices (IoTDs). However, the existing cryptographic and physical layer authentication schemes are unappealing in cross-domain scenarios due to the presence of resource-limited IoTDs, privacy concerns of cross-domain sharing, and the negative effect of malicious attackers. This paper proposes FedScatter, a lightweight cross-domain authentication scheme for securing wireless IoTDs using backscatter communication. First, device identity signatures are constructed by harnessing passive signal features generated from feather-light backscatter tags, incurring negligible overhead. Subsequently, a federated learning model is designed to aggregate device identity information across domains while respecting device heterogeneity and data privacy. A novel parameter aggregation algorithm is proposed to bolster authentication resilience and against malicious attacks to avoid model pollution by powerful attackers with substantial hardware resources. A FedScatter prototype is implemented and evaluated, demonstrating significant improvements over state-of-the-art works in both true positive rate and false positive rate under various attacks.
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Zhang et al. (2024) studied this question.
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