Real-Time Deep Learning-Based Anomaly Detection Approach for Multivariate Data Streams with Apache Flink

Abstract

For detecting anomalies which are unexpected behaviors in complex systems, deep learning-based anomaly detection algorithms for multivariate time series have gained a lot of attention recently. While many anomaly detection algorithms have been widely proposed, there has been no work on how to perform these detection algorithms for multivariate data streams with a stream processing framework. To address this issue, we present a real-time deep learning-based anomaly detection approach for multivariate data streams with Apache Flink. We train a LSTM encoder-decoder model to reconstruct a multivariate input sequence and develop a detection algorithm that uses reconstruction error between the input sequence and the reconstructed sequence. We show that our anomaly detection algorithm can provide promising performance on a real-world dataset. Then, we develop a Flink program by implementing three operators which process and transform multivariate data streams in a specific order. The Flink program outputs anomaly detection results in real time, making system experts can easily receive notices of critical issues and resolve the issues by appropriate actions to maintain the health of the systems.