Anomaly Detection on Time Series Data Using Apache Flink

Learn how to implement a real-time anomaly detection system for network security using Apache Flink in this 30-minute talk from Confluent. Explore the importance of detecting anomalies in network activity time series data as applications transition to cloud-native deployments. Discover how Apache Flink's ability to process continuous data streams in a stateful manner makes it ideal for analyzing time series data. Walk through the implementation steps and compare algorithms such as Exponentially Weighted Moving Average (EWMA) and Probabilistic EWMA (PEWMA) based on academic research. Gain insights into leveraging Apache Flink's framework and distributed processing engine for stateful computations over unbounded and bounded data streams to enhance network security in cloud-native environments.

Anomaly Detection on Time Series Data Using Apache Flink