| Title | Graph-Based Time Series Edge Anomaly Detection in Smart Grid |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Xu, Aidong, Wu, Tao, Zhang, Yunan, Hu, Zhiwei, Jiang, Yixin |
| Conference Name | 2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) |
| Keywords | Adaptation models, anomaly detection, composability, Computational modeling, Conferences, Data models, edge computing, edge detection, GCNs, Image edge detection, Metrics, pubcrawl, resilience, Resiliency, Scalability, security, Smart grids, time series, Time series analysis |
| Abstract | With the popularity of smart devices in the power grid and the advancement of data collection technology, the amount of electricity usage data has exploded in recent years, which is beneficial for optimizing service quality and grid operation. However, current data analysis is mainly based on cloud platforms, which poses challenges to transmission bandwidth, computing resources, and transmission delays. To solve the problem, this paper proposes a graph convolution neural networks (GCNs) based edge-cloud collaborative anomaly detection model. Specifically, the time series is converted into graph data based on visibility graph model, and graph convolutional network model is adopted to classify the labeled graph data for anomaly detection. Then a model segmentation method is proposed to adaptively divide the anomaly detection model between the edge equipment and the back-end server. Experimental results show that the proposed scheme provides an effective solution to edge anomaly detection and can make full use of the computing resources of terminal equipment. |
| DOI | 10.1109/BigDataSecurityHPSCIDS52275.2021.00012 |
| Citation Key | xu_graph-based_2021 |
Graph-Based Time Series Edge Anomaly Detection in Smart Grid