A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networks

TL;DR

This paper introduces a novel spatiotemporal correlation anomaly detection method for wireless sensor networks that combines contrastive and few-shot learning, effectively handling unlabeled data and class imbalance.

Contribution

It proposes a new model architecture with a two-stage training strategy that integrates contrastive learning and few-shot learning for improved anomaly detection in WSNs.

Findings

Achieved an F1 score of 90.97% on real datasets.

Outperformed existing supervised anomaly detection methods.

Effectively addressed label scarcity and class imbalance issues.

Abstract

Detecting anomalies in the data collected by WSNs can provide crucial evidence for assessing the reliability and stability of WSNs. Existing methods for WSN anomaly detection often face challenges such as the limited extraction of spatiotemporal correlation features, the absence of sample labels, few anomaly samples, and an imbalanced sample distribution. To address these issues, a spatiotemporal correlation detection model (MTAD-RD) considering both model architecture and a two-stage training strategy perspective is proposed. In terms of model structure design, the proposed MTAD-RD backbone network includes a retentive network (RetNet) enhanced by a cross-retention (CR) module, a multigranular feature fusion module, and a graph attention network module to extract internode correlation information. This proposed model can integrate the intermodal correlation features and spatial features of WSN neighbor nodes while extracting global information from time series data. Moreover, its serialized inference characteristic can remarkably reduce inference overhead. For model training, a two-stage training approach was designed. First, a contrastive learning proxy task was designed for time series data with graph structure information in WSNs, enabling the backbone network to learn transferable features from unlabeled data using unsupervised contrastive learning methods, thereby addressing the issue of missing sample labels in the dataset. Then, a caching-based sample sampler was designed to divide samples into few-shot and contrastive learning data. A specific joint loss function was developed to jointly train the dual-graph discriminator network to address the problem of sample imbalance effectively. In experiments carried out on real public datasets, the designed MTAD-RD anomaly detection method achieved an F1 score of 90.97%, outperforming existing supervised WSN anomaly detection methods.