Graph Isomorphism Networks for Wireless Link Layer Anomaly Classification

Nowadays, modern man-made infrastructures are being upgraded with information and communication technologies that form large wireless networks. Such large wireless networks must be monitored to ensure reliable operation by the proactive detection and correction of link failures or abnormal network behaviour in view of uninterrupted business operations. In this paper, we present a new method for detecting wireless link anomalies based on graph neural networks. The proposed method transforms time series data into graphs using a Markov Transition Field transformation. The data resulting from the transformation then trains a new graph neural network architecture to learn to successfully discriminate between 4 different link layer anomalies with an average F1 score of 0.956. The resulting model achieves competitive results with superior detection ability for the more subtle slow degradation anomaly while having up to ≈230 times fewer trainable parameters compared to the state-of-the-art, which subsequently makes it computationally much more efficient.