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Querying Streaming System Monitoring Data for Enterprise System Anomaly Detection by Gao, Peng; Xiao, Xusheng; Li, Ding; Jee, Kangkook; Chen, Haifeng; Kulkarni, Sanjeev R.; Mittal, Prateek is a scholarly article available to read on EtoBox.

What is Querying Streaming System Monitoring Data for Enterprise System Anomaly Detection about?

The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely abnormal system behavior detection over the stream of monitoring data. However, existing stream-based solutions lack explicit language constructs for expressing anomaly models that capture abnormal system behaviors, thus facing challenges in incorporating expert knowledge to perform timely anomaly detection over the large-scale monitoring data. To address these limitations, we build SAQL, a novel stream-based query system that takes as input, a real-time event feed aggregated from multiple hosts in an enterprise, and provides an anomaly query engine that queries the event feed to identify abnormal behaviors based on the specified anomaly models. SAQL provides a domain-specific query language, Stream-based Anomaly Query Language (SAQL), that uniquely integrates critical primitives for expressing major types of anomaly models. In the demo, we aim to show the complete usage scenario of SAQL by (1) performing an APT attack in a controlled environment, and (2) using SAQL to detect the abnormal behaviors in real

Author

Gao, Peng; Xiao, Xusheng; Li, Ding; Jee, Kangkook; Chen, Haifeng; Kulkarni, Sanjeev R.; Mittal, Prateek

Published

2019

Language

EN