mlsanityData to 14 Oct 2026

A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network

arXiv 2202.10454 ↗First posted 19 Feb 2022cs.LG cs.AIcited by 3

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Abstract (arXiv)

Anomaly detection is widely used to distinguish system anomalies by analyzing the temporal and spatial features of wireless sensor network (WSN) data streams; it is one of critical technique that ensures the reliability of WSNs. Currently, graph neural networks (GNNs) have become popular state-of-the-art methods for conducting anomaly detection on WSN data streams. However, the existing anomaly detection methods based on GNNs do not consider the temporal and spatial features of WSN data streams simultaneously, such as multi-node, multi-modal and multi-time features, seriously impacting their effectiveness. In this paper, a novel anomaly detection model is proposed for multimodal WSN data flows, where three GNNs are used to separately extract the temporal features of WSN data flows, the correlation features between different modes and the spatial features between sensor node positions. Specifically, first, the temporal features and modal correlation features extracted from each sensor node are fused into one vector representation, which is further aggregated with the spatial features, i.e., the spatial position relationships of the nodes; finally, the current time-series data of WSN nodes are predicted, and abnormal states are identified according to the fusion features. The simulation results obtained on a public dataset show that the proposed approach is able to significantly improve upon the existing methods in terms of its robustness, and its F1 score reaches 0.90, which is 14.2% higher than that of the graph convolution network (GCN) with long short-term memory (LSTM).

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 44 references that are in the map. First posted on arXiv 19 Feb 2022.

Builds on

13 of 44 references.

References · 13

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  5. 2018Deep Graph Infomaxcited by 1,106
  6. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  7. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  8. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  9. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  10. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 167
  11. 2020ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networkscited by 24
  12. 2019On Defending Against Label Flipping Attacks on Malware Detection Systemscited by 22
1 more
  1. 2018Review of Deep Learningcited by 5

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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2.

3 papers.

  1. 2022A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networkscited by 0
  2. 2025A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Networkcited by 0
  3. 2025A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networkscited by 0