An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data

• URL: http://arxiv.org/abs/2609.03505v1
• Date: Thu, 03 Sep 2026 08:07:06 GMT
• Title: An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data
• Abstract summary: Anomaly detection in Internet of Things networks presents unique challenges due to diversity of devices, lack of labeled data, and domain variability across environments.<n>We propose a novel framework for multivariate time-series anomaly detection that leverages adversarial learning and contrastive loss within a sequence-based Variational Autoencoder architecture.
• Score: 7.858819231575403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation [61.70497741321102]
  Test-time adaptation aims to align a model to shifting test domains using only unlabeled streaming data.<n>Most existing methods implicitly infer a single global domain distribution, ignoring the multidimensional and sample-specific nature of real-world domain shifts.<n>We propose DOME, an effective domain encoder that explicitly models each sample's domain in a zero-shot manner.
  arXiv  Detail & Related papers  (2026-06-02T03:24:03Z)
• HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models [50.18051979020699]
  End-to-end autonomous driving has emerged as a compelling alternative to traditional modular pipelines.<n>We propose a trajectory-driven learning paradigm that organizes training around planning trajectories.<n>We show that a single unified model can be trained on heterogeneous datasets while maintaining strong performance within each domain.
  arXiv  Detail & Related papers  (2026-05-19T10:12:01Z)
• Exploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection [46.724153214302184]
  Cross-domain intrusion detection remains a critical challenge due to significant variability in network traffic characteristics and feature distributions across environments.<n>This study evaluates the transferability of three widely used flow-based feature sets (Argus, Zeek and CICFlowMeter) across four widely used datasets representing heterogeneous IoT and Industrial IoT network conditions.
  arXiv  Detail & Related papers  (2026-02-27T10:16:28Z)
• FSCA-Net: Feature-Separated Cross-Attention Network for Robust Multi-Dataset Training [3.2658295979028753]
  We propose a unified framework that disentangles feature representations into domain-invariant and domain-specific components.<n>A novel cross-attention fusion module adaptively models interactions between these components, ensuring effective knowledge transfer.<n>Experiments on multiple crowd counting benchmarks demonstrate that FSCA-Net effectively mitigates negative transfer and achieves state-of-the-art cross-dataset generalization.
  arXiv  Detail & Related papers  (2026-02-02T02:18:48Z)
• Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose Estimation [50.31351006532924]
  Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc.<n>It suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation.<n>We introduce a novel framework that capitalizes on both representation aggregation and segregation for domain adaptive human pose estimation.
  arXiv  Detail & Related papers  (2024-12-29T17:59:45Z)
• Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification [57.945437355714155]
  Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions.<n>Existing approaches focus on single-source domain generalization to unseen target domains.<n>We propose a novel multi-source collaborative domain generalization framework (MS-CDG) based on homogeneity and heterogeneity characteristics of multi-source remote sensing data.
  arXiv  Detail & Related papers  (2024-12-05T06:15:08Z)
• Robust Unsupervised Domain Adaptation by Retaining Confident Entropy via Edge Concatenation [7.953644697658355]
  Unsupervised domain adaptation can mitigate the need for extensive pixel-level annotations to train semantic segmentation networks. We introduce a novel approach to domain adaptation, leveraging the synergy of internal and external information within entropy-based adversarial networks. We devised a probability-sharing network that integrates diverse information for more effective segmentation.
  arXiv  Detail & Related papers  (2023-10-11T02:50:16Z)
• Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment [17.086123737443714]
  Anomaly segmentation plays a pivotal role in identifying atypical objects in images, crucial for hazard detection in autonomous driving systems. While existing methods demonstrate noteworthy results on synthetic data, they often fail to consider the disparity between synthetic and real-world data domains. We introduce the Multi-Granularity Cross-Domain Alignment framework, tailored to harmonize features across domains at both the scene and individual sample levels.
  arXiv  Detail & Related papers  (2023-08-16T22:54:49Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Spatial Attention Pyramid Network for Unsupervised Domain Adaptation [66.75008386980869]
  Unsupervised domain adaptation is critical in various computer vision tasks. We design a new spatial attention pyramid network for unsupervised domain adaptation. Our method performs favorably against the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-03-29T09:03:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.