无线传感器网络中融合对比学习与少样本学习的新型时空相关性异常检测方法
A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networks
AI总结：
针对WSN异常检测中时空特征提取受限、标签缺失及样本不均等问题，提出融合RetNet与图注意力网络的MTAD-RD模型，采用对比学习与少样本联合训练策略，在真实数据集上F1达90.97%，优于现有方法。
摘要：
检测WSN收集数据中的异常，可为评估WSN的可靠性和稳定性提供关键证据。现有的WSN异常检测方法常面临时空相关性特征提取受限、样本标签缺失、异常样本少以及样本分布不均衡等挑战。为解决这些问题，提出了一种从模型架构和两阶段训练策略角度考虑的时空相关性检测模型（MTAD-RD）。在模型结构设计方面，提出的MTAD-RD骨干网络包括由交叉保留（CR）模块增强的保留网络（RetNet）、多粒度特征融合模块以及图注意力网络模块，用于提取节点间相关性信息。该模型能在从时间序列数据中提取全局信息的同时，整合WSN邻居节点的模态间相关性特征和空间特征。此外，其序列化推理特性可显著降低推理开销。在模型训练方面，设计了两阶段训练方法。首先，为WSN中带图结构信息的时间序列数据设计了对比学习代理任务，使骨干网络能利用无监督对比学习方法从无标签数据中学习可迁移特征，从而解决数据集中样本标签缺失的问题。然后，设计了基于缓存的样本采样器，将样本分为少样本数据和对比学习数据。开发了特定的联合损失函数来联合训练双图判别器网络，以有效解决样本不均衡问题。在真实公开数据集上进行的实验中，设计的MTAD-RD异常检测方法取得了90.97%的F1分数，优于现有的有监督WSN异常检测方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
