Anomaly Prediction With Hybrid Supervised/Unsupervised Deep Learning for Elastic Optical Networks: A Multi-Index Correlative Approach

With the emergence of new services, the complex optical network environment makes it more difficult to predict network anomalies. This paper proposes a multi-index anomaly prediction scheme with hybrid supervised/unsupervised deep learning for elastic optical networks. Aimed at complex optical network indicators, the scheme presents three phases to enhance the abnormal prediction. The scheme first selects the most influential indicators of anomaly label among the mass of network indicators by calculating the Spearman correlation coefficient. Then, considering the timeliness of network data, it predicts time series of different indicators to analyze future network conditions by using long short-term memory neural network. In order to improve the accuracy and efficiency of the anomaly detection model, the scheme further establishes a deep neural network for anomaly classification. We also discuss how to process data without anomaly labels. The feasibility of the proposed scheme is verified on a real network dataset. Experimental results show that the scheme can predict the occurrence of future network anomalies with high accuracy, protect network services from potential abnormalities, and enhance the stability and robustness of the network.