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This paper introduces a novel adversarial zero-shot learning framework for anomaly detection in multivariate IoT traffic data, addressing the challenges of device diversity and lack of labeled data. By employing a sequence-based Variational Autoencoder (VAE) architecture with contrastive loss, the method achieves domain-invariant latent representations and structured embeddings without the need for labeled data. The results showcase strong zero-shot generalization across six diverse datasets, outperforming a baseline in realistic IoT environments characterized by heterogeneity and privacy constraints.
Zero-shot learning in IoT anomaly detection can now achieve strong generalization across diverse environments without labeled data, challenging traditional approaches.
Anomaly detection in Internet of Things (IoT) networks presents unique challenges due to the diversity of devices, lack of labeled data, and domain variability across environments. In this paper, we propose a novel framework for multivariate time-series anomaly detection that leverages adversarial learning and contrastive loss within a sequence-based Variational Autoencoder (VAE) architecture. Our method enables zero-shot domain adaptation by jointly optimizing domain-invariant latent representations and semantically structured embedding spaces, without requiring labeled data or raw feature transfer. To address the heterogeneity of IoT deployments, we introduce encoder and decoder adaptor layers that align feature distributions across domains while preserving contextual semantics. Additionally, we propose a destination-based segmentation strategy to better model real-world communication structures in IoT traffic. Our framework is comprehensively evaluated on six distinct datasets spanning industrial, enterprise, general-purpose, smart home, and military automation domains across 44 transfer scenarios. Experimental results demonstrate strong zero-shot generalization in several cross-domain settings and competitive performance against a contrastive domain-adaptation baseline under realistic, heterogeneous, and privacy-constrained IoT conditions.