Research Article
A Novel Semi-supervised IoT Time Series Anomaly Detection Model Using Graph Structure Learning
@INPROCEEDINGS{10.1007/978-3-031-54528-3_21, author={Weijian Song and Peng Chen and Juan Chen and Yunni Xia and Xi Li and Qinghui Xi and Hongxia He}, title={A Novel Semi-supervised IoT Time Series Anomaly Detection Model Using Graph Structure Learning}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2024}, month={2}, keywords={IoT Time Series Anomaly Detection Graph Structure Learning Graph Convolutional Networks Semi-supervised Mean Teachers}, doi={10.1007/978-3-031-54528-3_21} }- Weijian Song
Peng Chen
Juan Chen
Yunni Xia
Xi Li
Qinghui Xi
Hongxia He
Year: 2024
A Novel Semi-supervised IoT Time Series Anomaly Detection Model Using Graph Structure Learning
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-031-54528-3_21
Abstract
Internet of Things (IoT) is an evolving paradigm for building smart cross-industry. The data gathered from IoT devices may have anomalies or other errors for various reasons, such as malicious activities or sensor failures. Anomaly detection is thus in high need for guaranteeing trustworthy execution of IoT applications. Existing IoT anomaly detection methods are usually built upon unsupervised methods and thus can be inadequate when facing complex IoT data regularity. In this article, we propose a semi-supervised approach for detecting IoT time series anomalies based on Graph Structure Learning (GSL) using multi-layer perceptron Graph Convolutional Networks (GCN) and the Mean Teachers (MT) mechanism. The proposed model is capable of leveraging a small amount of labeled data (1% to 10%) to achieve high detection accuracy. We adopt Mean Teachers to utilize unlabeled data for enhancing the model’s detection performance. Moreover, we design a novel graph structure learning layer to adaptively capture the IoT data features among different nodes. Experimental results clearly suggest that the proposed model outperforms its competitors on two public IoT datasets, achieving 82.85% in terms of F1 score and 22.8% increase.
