A Deep Learning Approach for Detecting Virtual Link Anomalies in LEO Satellite Networks

This paper proposes a deep learning (DL)-based time series (TS) anomaly detection method (DLTS) for the low earth orbit (LEO) satellite network slicing scenario, aiming to address the virtual link anomalies induced by software and hardware abnormalities. Initially, the time series anomalous variations of each resource utilization in satellite network slicing are categorized into three types based on the utilization of computing, storage, and network resources of virtual nodes. Thereafter, the anomaly detection problem is formulated as a classification problem, and the time series are transformed into images using the Gramian Angular Field (GAF) for model input. Lastly, we propose a design principle for a time-constrained deep neural network architecture to mitigate training time, and design a DL model architecture to classify the TS transformation images of resource utilization for each virtual node. This aligns with the objective of the satellite network slicing scenario. Additionally, a new evaluation metric is introduced. Experimental results underscore the shorter training time of the proposed model, and affirm its efficacy, demonstrated through accuracy, F1 score, and the newly proposed evaluation metric.