mlsanityData to 14 Oct 2026

A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks

arXiv 2212.13904 ↗First posted 26 Dec 2022cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed. This method integrates temporal WSN data flow feature extraction, spatial position feature extraction and intermodal WSN correlation feature extraction into the design of the autoencoder to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network (GNN) is used to introduce the WSN topology from a global spatial perspective for anomaly detection and extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, the adaptive fusion method involving weighted summation is used to extract the relevant features between different models. In addition, this paper introduces a gated recurrent unit (GRU) to solve the long-term dependence problem of the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability of the current system. Since the spatial feature extraction operation is advanced, the designed method can be applied to the task of large-scale network anomaly detection by adding a clustering operation. Experiments show that the designed method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2% higher than those of the existing anomaly detection methods based on unsupervised reconstruction and prediction. Code and model are available at https://github.com/GuetYe/anomaly_detection/GLSL

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 37 references that are in the map. First posted on arXiv 26 Dec 2022.

Builds on

16 of 37 references.

References · 16

  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. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  7. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  8. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  9. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  10. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  11. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 167
  12. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 155
4 more
  1. 2020ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networkscited by 24
  2. 2022Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detectioncited by 18
  3. 2018Review of Deep Learningcited by 5
  4. 2022A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Networkcited by 3

Built on by

Not cited yet.