AI 中文总结
该研究针对物联网异常检测的设备多样、缺标注数据等挑战，提出结合对抗学习与对比损失的序列VAE框架，通过适配层、分割策略实现零样本跨域异常检测，在44种场景的6类数据集上表现出强泛化性与竞争力。
AI 中文摘要
物联网（IoT）网络中的异常检测面临独特挑战，原因在于设备多样性、标注数据匮乏以及不同环境间的领域变异性。本文提出一种用于多变量时间序列异常检测的新型框架，该框架在基于序列的变分自编码器（VAE）架构中利用对抗学习和对比损失。我们的方法通过联合优化领域不变的潜在表示和语义结构化嵌入空间，实现零样本领域自适应，无需标注数据或原始特征迁移。为解决物联网部署的异质性，我们引入编码器和解码器适配层，在保留上下文语义的同时对齐跨领域的特征分布。此外，我们提出一种基于目的地的分割策略，以更好地建模物联网流量中的真实通信结构。我们的框架在涵盖工业、企业、通用、智能家居和军事自动化领域的六个不同数据集上，于44种迁移场景中接受全面评估。实验结果表明，该框架在若干跨领域设置中展现出强大的零样本泛化能力，且在现实、异质性及隐私受限的物联网条件下，其性能与对比领域自适应基线相比具有竞争力。
英文摘要
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.
