Kronos: Lightweight Knowledge-based Event Analysis in Cyber-Physical Data Streams

摘要

We demonstrate Kronos, a framework and system that automatically extracts highly dynamic knowledge for complex event analysis in Cyber-Physical systems. Kronos captures events with anomaly-based event model, and integrates various events by correlating with their temporal associations in realtime, from heterogeneous, continuous cyber-physical measurement data streams. It maintains a lightweight highly dynamic knowledge base, enabled by online, window-based ensemble learning and incremental association analysis for event detection and linkage, respectively. These algorithms incur time costs determined by available memory, independent of the size of streams. Exploiting the highly dynamic knowledge, Kronos supports a rich set of stream event analytical queries including event search (keywords and query-by-example), provenance queries ("which measurements or features are responsible for detected events?"), and root cause analysis. We demonstrate how the GUI of Kronos interacts with users to support both continuous and ad-hoc queries online and enables situational awareness in Cyber-power systems, communication, and traffic networks.