Detection and analysis of real-time anomalies in large-scale complex system

Abstract

For data-driven anomaly detection, it is difficult to model a prediction model with high accuracy and sensitivity to anomalous states. In order to solve the above problems, this paper proposes an improved multivariate Transfer Entropy method to judge the physical causality between high-dimensional time series. According to the causality, combined with the LSTM (Long Short-Term Memory) model, the CF-LSTM (causality features-LSTM) model is proposed. The CF-LSTM model uses the causality features of the parameter to make predictions. Compared with other models, the CF-LSTM model improves the prediction accuracy and it is sensitive to anomalies. Based on this, an anomaly detection algorithm is proposed. Case analysis based on the actual data shows that the detection precision, recall, and F1-score (a measure of classification problem) of this method are improved compared with other anomaly detection models, which illustrates the effectiveness of this method.