FCCE: Highly Scalable Distributed Feature Collection and Correlation Engine for Low Latency Big Data Analytics

In this paper, we present the design, architecture, and implementation of a novel analysis engine, called FCCE, that finds correlations across a diverse set of data types spanning over large time windows with very small latency and with minimal access to raw data. FCCE scales well to collecting, extracting, and querying features from geographically distributed large data sets. FCCE has been deployed in a large production network with over 150,000 workstations for over 2 years, ingesting more than 2 billion events per day and providing low latency query responses for various analytics. We explore two real use cases and applications to demonstrate how we utilize the deployment of FCCE on large diverse data sets in the cyber security domain: 1) detecting fluxing domain names of potential botnet activity and identifying all the devices in the production network querying these names, and 2) detecting advanced persistent threat infection. Both evaluation results and our experience with real-world applications show that FCCE yields superior performance over existing approaches, and excels in the challenging cyber security domain by correlating multiple features and deriving security intelligence.