Sharing Model State Between Real-Time and Batch Paths in Network Security Anomaly Detection

摘要

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

主权项

1. A method comprising:
implementing a real-time event processing engine on a distributed data processing platform, wherein the real-time event processing engine is configured to process an unbounded stream of event data to detect a plurality of network security-related issues and/or to train a machine learning model; implementing a batch event processing engine on the distributed data processing platform, wherein the batch event processing engine is configured to process a batch of historic event data to detect a plurality of network security-related issues and/or to train a machine learning model; and enabling the real-time event processing engine and the batch event processing engine to share a model state of a particular machine learning model, the particular machine learning model being configured to process a time slice of data to produce a score for detecting a network security-related issue.