计算机工程与科学2025，Vol.47Issue(5)：843-850,8.DOI:10.3969/j.issn.1007-130X.2025.05.008
基于GATv2-TCN联合优化的WSN数据流异常检测算法
A WSN data stream anomaly detection algorithm based on GATv2-TCN joint optimization
摘要
Abstract
In sensor networks,anomaly detection in data streams enables timely fault detection and alerting,ensuring the safe and reliable operation of the system.However,WSN(Wireless Sensor Net-work)data stream anomaly detection still faces two major challenges:1)the complex correlations a-mong different time series need to be further explored;2)anomaly samples in datasets with extremely unbalanced normal/anomaly distributions are difficult to detect.This paper proposes an anomaly detec-tion algorithm based on GATv2-TCN(Graph Attention Network version 2-Temporal Convolutional Net-work).GATv2 and TCN are used to model complex relationships in both feature and temporal dimen-sions,and the prediction and reconstruction modules are optimized.Four datasets are employed to vali-date and analyze the performance of the proposed algorithm.Experiments show that the proposed algo-rithm achieves high F1 and AUC scores,particularly outperforming baseline models across various met-rics for unbalanced datasets,demonstrating its effectiveness in WSN data stream anomaly detection.关键词
无线传感器网络/数据流异常检测/GATv2/TCNKey words
wireless sensor network/data flow anomaly detection/GATv2/TCN分类
信息技术与安全科学引用本文复制引用
苏宇杭,马俊,樊津瑜,陈博行,周家城,尹博然..基于GATv2-TCN联合优化的WSN数据流异常检测算法[J].计算机工程与科学,2025,47(5):843-850,8.基金项目
青海省自然科学基金(2021-ZJ-916) （2021-ZJ-916）
